Tag Archives: #AutoML

Improved On-Device ML on Pixel 6, with Neural Architecture Search

This fall Pixel 6 phones launched with Google Tensor, Google’s first mobile system-on-chip (SoC), bringing together various processing components (such as central/graphic/tensor processing units, image processors, etc.) onto a single chip, custom-built to deliver state-of-the-art innovations in machine learning (ML) to Pixel users. In fact, every aspect of Google Tensor was designed and optimized to run Google’s ML models, in alignment with our AI Principles. That starts with the custom-made TPU integrated in Google Tensor that allows us to fulfill our vision of what should be possible on a Pixel phone.

Today, we share the improvements in on-device machine learning made possible by designing the ML models for Google Tensor’s TPU. We use neural architecture search (NAS) to automate the process of designing ML models, which incentivize the search algorithms to discover models that achieve higher quality while meeting latency and power requirements. This automation also allows us to scale the development of models for various on-device tasks. We’re making these models publicly available through the TensorFlow model garden and TensorFlow Hub so that researchers and developers can bootstrap further use case development on Pixel 6. Moreover, we have applied the same techniques to build a highly energy-efficient face detection model that is foundational to many Pixel 6 camera features.

An illustration of NAS to find TPU-optimized models. Each column represents a stage in the neural network, with dots indicating different options, and each color representing a different type of building block. A path from inputs (e.g., an image) to outputs (e.g., per-pixel label predictions) through the matrix represents a candidate neural network. In each iteration of the search, a neural network is formed using the blocks chosen at every stage, and the search algorithm aims to find neural networks that jointly minimize TPU latency and/or energy and maximize accuracy.

Search Space Design for Vision Models
A key component of NAS is the design of the search space from which the candidate networks are sampled. We customize the search space to include neural network building blocks that run efficiently on the Google Tensor TPU.

One widely-used building block in neural networks for various on-device vision tasks is the Inverted Bottleneck (IBN). The IBN block has several variants, each with different tradeoffs, and is built using regular convolution and depthwise convolution layers. While IBNs with depthwise convolution have been conventionally used in mobile vision models due to their low computational complexity, fused-IBNs, wherein depthwise convolution is replaced by a regular convolution, have been shown to improve the accuracy and latency of image classification and object detection models on TPU.

However, fused-IBNs can have prohibitively high computational and memory requirements for neural network layer shapes that are typical in the later stages of vision models, limiting their use throughout the model and leaving the depthwise-IBN as the only alternative. To overcome this limitation, we introduce IBNs that use group convolutions to enhance the flexibility in model design. While regular convolution mixes information across all the features in the input, group convolution slices the features into smaller groups and performs regular convolution on features within that group, reducing the overall computational cost. Called group convolution–based IBNs (GC-IBNs), their tradeoff is that they may adversely impact model quality.

Inverted bottleneck (IBN) variants: (a) depthwise-IBN, depthwise convolution layer with filter size KxK sandwiched between two convolution layers with filter size 1x1; (b) fused-IBN, convolution and depthwise are fused into a convolution layer with filter size KxK; and (c) group convolution–based GC-IBN that replaces with the KxK regular convolution in fused-IBN with group convolution. The number of groups (group count) is a tunable parameter during NAS.
Inclusion of GC-IBN as an option provides additional flexibility beyond other IBNs. Computational cost and latency of different IBN variants depends on the feature dimensions being processed (shown above for two example feature dimensions). We use NAS to determine the optimal choice of IBN variants.

Faster, More Accurate Image Classification
Which IBN variant to use at which stage of a deep neural network depends on the latency on the target hardware and the performance of the resulting neural network on the given task. We construct a search space that includes all of these different IBN variants and use NAS to discover neural networks for the image classification task that optimize the classification accuracy at a desired latency on TPU. The resulting MobileNetEdgeTPUV2 model family improves the accuracy at a given latency (or latency at a desired accuracy) compared to the existing on-device models when run on the TPU. MobileNetEdgeTPUV2 also outperforms their predecessor, MobileNetEdgeTPU, the image classification models designed for the previous generation of the TPU.

Network architecture families visualized as connected dots at different latency targets. Compared with other mobile models, such as FBNet, MobileNetV3, and EfficientNets, MobileNetEdgeTPUV2 models achieve higher ImageNet top-1 accuracy at lower latency when running on Google Tensor’s TPU.

MobileNetEdgeTPUV2 models are built using blocks that also improve the latency/accuracy tradeoff on other compute elements in the Google Tensor SoC, such as the CPU. Unlike accelerators such as the TPU, CPUs show a stronger correlation between the number of multiply-and-accumulate operations in the neural network and latency. GC-IBNs tend to have fewer multiply-and-accumulate operations than fused-IBNs, which leads MobileNetEdgeTPUV2 to outperform other models even on Pixel 6 CPU.

MobileNetEdgeTPUV2 models achieve ImageNet top-1 accuracy at lower latency on Pixel 6 CPU, and outperform other CPU-optimized model architectures, such as MobileNetV3.

Improving On-Device Semantic Segmentation
Many vision models consist of two components, the base feature extractor for understanding general features of the image, and the head for understanding domain-specific features, such as semantic segmentation (the task of assigning labels, such as sky, car, etc., to each pixel in an image) and object detection (the task of detecting instances of objects, such as cats, doors, cars, etc., in an image). Image classification models are often used as feature extractors for these vision tasks. As shown below, the MobileNetEdgeTPUV2 classification model coupled with the DeepLabv3+ segmentation head improves the quality of on-device segmentation.

To further improve the segmentation model quality, we use the bidirectional feature pyramid network (BiFPN) as the segmentation head, which performs weighted fusion of different features extracted by the feature extractor. Using NAS we find the optimal configuration of blocks in both the feature extractor and the BiFPN head. The resulting models, named Autoseg-EdgeTPU, produce even higher-quality segmentation results, while also running faster.

The final layers of the segmentation model contribute significantly to the overall latency, mainly due to the operations involved in generating a high resolution segmentation map. To optimize the latency on TPU, we introduce an approximate method for generating the high resolution segmentation map that reduces the memory requirement and provides a nearly 1.5x speedup, without significantly impacting the segmentation quality.

Left: Comparing the performance, measured as mean intersection-over-union (mIOU), of different segmentation models on the ADE20K semantic segmentation dataset (top 31 classes). Right: Approximate feature upsampling (e.g., increasing resolution from 32x32 → 512x512). Argmax operation used to compute per-pixel labels is fused with the bilinear upsampling. Argmax performed on smaller resolution features reduces memory requirements and improves latency on TPU without a significant impact to quality.

Higher-Quality, Low-Energy Object Detection
Classic object detection architectures allocate ~70% of the compute budget to the feature extractor and only ~30% to the detection head. For this task we incorporate the GC-IBN blocks into a search space we call “Spaghetti Search Space”1, which provides the flexibility to move more of the compute budget to the head. This search space also uses the non-trivial connection patterns seen in recent NAS works such as MnasFPN to merge different but related stages of the network to strengthen understanding.

We compare the models produced by NAS to MobileDet-EdgeTPU, a class of mobile detection models customized for the previous generation of TPU. MobileDets have been demonstrated to achieve state-of-the-art detection quality on a variety of mobile accelerators: DSPs, GPUs, and the previous TPU. Compared with MobileDets, the new family of SpaghettiNet-EdgeTPU detection models achieves +2.2% mAP (absolute) on COCO at the same latency and consumes less than 70% of the energy used by MobileDet-EdgeTPU to achieve similar accuracy.

Comparing the performance of different object detection models on the COCO dataset with the mAP metric (higher is better). SpaghettiNet-EdgeTPU achieves higher detection quality at lower latency and energy consumption compared to previous mobile models, such as MobileDets and MobileNetV2 with Feature Pyramid Network (FPN).

Inclusive, Energy-Efficient Face Detection
Face detection is a foundational technology in cameras that enables a suite of additional features, such as fixing the focus, exposure and white balance, and even removing blur from the face with the new Face Unblur feature. Such features must be designed responsibly, and Face Detection in the Pixel 6 were developed with our AI Principles top of mind.

Left: The original photo without improvements. Right: An unblurred face in a dynamic environment. This is the result of Face Unblur combined with a more accurate face detector running at a higher frames per second.

Since mobile cameras can be power-intensive, it was important for the face detection model to fit within a power budget. To optimize for energy efficiency, we used the Spaghetti Search Space with an algorithm to search for architectures that maximize accuracy at a given energy target. Compared with a heavily optimized baseline model, SpaghettiNet achieves the same accuracy at ~70% of the energy. The resulting face detection model, called FaceSSD, is more power-efficient and accurate. This improved model, combined with our auto-white balance and auto-exposure tuning improvements, are part of Real Tone on Pixel 6. These improvements help better reflect the beauty of all skin tones. Developers can utilize this model in their own apps through the Android Camera2 API.

Toward Datacenter-Quality Language Models on a Mobile Device
Deploying low-latency, high-quality language models on mobile devices benefits ML tasks like language understanding, speech recognition, and machine translation. MobileBERT, a derivative of BERT, is a natural language processing (NLP) model tuned for mobile CPUs.

However, due to the various architectural optimizations made to run these models efficiently on mobile CPUs, their quality is not as high as that of the large BERT models. Since MobileBERT on TPU runs significantly faster than on CPU, it presents an opportunity to improve the model architecture further and reduce the quality gap between MobileBERT and BERT. We extended the MobileBERT architecture and leveraged NAS to discover models that map well to the TPU. These new variants of MobileBERT, named MobileBERT-EdgeTPU, achieve up to 2x higher hardware utilization, allowing us to deploy large and more accurate models on TPU at latencies comparable to the baseline MobileBERT.

MobileBERT-EdgeTPU models, when deployed on Google Tensor’s TPU, produce on-device quality comparable to the large BERT models typically deployed in data centers.

Performance on the question answering task (SQuAD v 1.1). While the TPU in Pixel 6 provides a ~10x acceleration over CPU, further model customization for the TPU achieves on-device quality comparable to the large BERT models typically deployed in data centers.

Conclusion
In this post, we demonstrated how designing ML models for the target hardware expands the on-device ML capabilities of Pixel 6 and brings high-quality, ML-powered experiences to Pixel users. With NAS, we scaled the design of ML models to a variety of on-device tasks and built models that provide state-of-the-art quality on-device within the latency and power constraints of a mobile device. Researchers and ML developers can try out these models in their own use cases by accessing them through the TensorFlow model garden and TF Hub.

Acknowledgements
This work is made possible through a collaboration spanning several teams across Google. We’d like to acknowledge contributions from Rachit Agrawal, Berkin Akin, Andrey Ayupov, Aseem Bathla, Gabriel Bender, Po-Hsein Chu, Yicheng Fan, Max Gubin, Jaeyoun Kim, Quoc Le, Dongdong Li, Jing Li, Yun Long, Hanxiao Lu, Ravi Narayanaswami, Benjamin Panning, Anton Spiridonov, Anakin Tung, Zhuo Wang, Dong Hyuk Woo, Hao Xu, Jiayu Ye, Hongkun Yu, Ping Zhou, and Yanqi Zhuo. Finally, we’d like to thank Tom Small for creating illustrations for this blog post.



1The resulting architectures tend to look like spaghetti because of the connection patterns formed between blocks. 

Source: Google AI Blog


Toward Fast and Accurate Neural Networks for Image Recognition

As neural network models and training data size grow, training efficiency is becoming an important focus for deep learning. For example, GPT-3 demonstrates remarkable capability in few-shot learning, but it requires weeks of training with thousands of GPUs, making it difficult to retrain or improve. What if, instead, one could design neural networks that were smaller and faster, yet still more accurate?

In this post, we introduce two families of models for image recognition that leverage neural architecture search, and a principled design methodology based on model capacity and generalization. The first is EfficientNetV2 (accepted at ICML 2021), which consists of convolutional neural networks that aim for fast training speed for relatively small-scale datasets, such as ImageNet1k (with 1.28 million images). The second family is CoAtNet, which are hybrid models that combine convolution and self-attention, with the goal of achieving higher accuracy on large-scale datasets, such as ImageNet21 (with 13 million images) and JFT (with billions of images). Compared to previous results, our models are 4-10x faster while achieving new state-of-the-art 90.88% top-1 accuracy on the well-established ImageNet dataset. We are also releasing the source code and pretrained models on the Google AutoML github.

EfficientNetV2: Smaller Models and Faster Training
EfficientNetV2 is based upon the previous EfficientNet architecture. To improve upon the original, we systematically studied the training speed bottlenecks on modern TPUs/GPUs and found: (1) training with very large image sizes results in higher memory usage and thus is often slower on TPUs/GPUs; (2) the widely used depthwise convolutions are inefficient on TPUs/GPUs, because they exhibit low hardware utilization; and (3) the commonly used uniform compound scaling approach, which scales up every stage of convolutional networks equally, is sub-optimal. To address these issues, we propose both a training-aware neural architecture search (NAS), in which the training speed is included in the optimization goal, and a scaling method that scales different stages in a non-uniform manner.

The training-aware NAS is based on the previous platform-aware NAS, but unlike the original approach, which mostly focuses on inference speed, here we jointly optimize model accuracy, model size, and training speed. We also extend the original search space to include more accelerator-friendly operations, such as FusedMBConv, and simplify the search space by removing unnecessary operations, such as average pooling and max pooling, which are never selected by NAS. The resulting EfficientNetV2 networks achieve improved accuracy over all previous models, while being much faster and up to 6.8x smaller.

To further speed up the training process, we also propose an enhanced method of progressive learning, which gradually changes image size and regularization magnitude during training. Progressive training has been used in image classification, GANs, and language models. This approach focuses on image classification, but unlike previous approaches that often trade accuracy for improved training speed, can slightly improve the accuracy while also significantly reducing training time. The key idea in our improved approach is to adaptively change regularization strength, such as dropout ratio or data augmentation magnitude, according to the image size. For the same network, small image size leads to lower network capacity and thus requires weak regularization; vice versa, a large image size requires stronger regularization to combat overfitting.

Progressive learning for EfficientNetV2. Here we mainly focus on three types of regularizations: data augmentation, mixup, and dropout.

We evaluate the EfficientNetV2 models on ImageNet and a few transfer learning datasets, such as CIFAR-10/100, Flowers, and Cars. On ImageNet, EfficientNetV2 significantly outperforms previous models with about 5–11x faster training speed and up to 6.8x smaller model size, without any drop in accuracy.

EfficientNetV2 achieves much better training efficiency than prior models for ImageNet classification.

CoAtNet: Fast and Accurate Models for Large-Scale Image Recognition
While EfficientNetV2 is still a typical convolutional neural network, recent studies on Vision Transformer (ViT) have shown that attention-based transformer models could perform better than convolutional neural networks on large-scale datasets like JFT-300M. Inspired by this observation, we further expand our study beyond convolutional neural networks with the aim of finding faster and more accurate vision models.

In “CoAtNet: Marrying Convolution and Attention for All Data Sizes”, we systematically study how to combine convolution and self-attention to develop fast and accurate neural networks for large-scale image recognition. Our work is based on an observation that convolution often has better generalization (i.e., the performance gap between training and evaluation) due to its inductive bias, while self-attention tends to have greater capacity (i.e., the ability to fit large-scale training data) thanks to its global receptive field. By combining convolution and self-attention, our hybrid models can achieve both better generalization and greater capacity.

Comparison between convolution, self-attention, and hybrid models. Convolutional models converge faster, ViTs have better capacity, while the hybrid models achieve both faster convergence and better accuracy.

We observe two key insights from our study: (1) depthwise convolution and self-attention can be naturally unified via simple relative attention, and (2) vertically stacking convolution layers and attention layers in a way that considers their capacity and computation required in each stage (resolution) is surprisingly effective in improving generalization, capacity and efficiency. Based on these insights, we have developed a family of hybrid models with both convolution and attention, named CoAtNets (pronounced “coat” nets). The following figure shows the overall CoAtNet network architecture:

Overall CoAtNet architecture. Given an input image with size HxW, we first apply convolutions in the first stem stage (S0) and reduce the size to H/2 x W/2. The size continues to reduce with each stage. Ln refers to the number of layers. Then, the early two stages (S1 and S2) mainly adopt MBConv building blocks consisting of depthwise convolution. The later two stages (S3 and S4) mainly adopt Transformer blocks with relative self-attention. Unlike the previous Transformer blocks in ViT, here we use pooling between stages, similar to Funnel Transformer. Finally, we apply a classification head to generate class prediction.

CoAtNet models consistently outperform ViT models and its variants across a number of datasets, such as ImageNet1K, ImageNet21K, and JFT. When compared to convolutional networks, CoAtNet exhibits comparable performance on a small-scale dataset (ImageNet1K) and achieves substantial gains as the data size increases (e.g. on ImageNet21K and JFT).

Comparison between CoAtNet and previous models after pre-training on the medium sized ImageNet21K dataset. Under the same model size, CoAtNet consistently outperforms both ViT and convolutional models. Noticeably, with only ImageNet21K, CoAtNet is able to match the performance of ViT-H pre-trained on JFT.

We also evaluated CoAtNets on the large-scale JFT dataset. To reach a similar accuracy target, CoAtNet trains about 4x faster than previous ViT models and more importantly, achieves a new state-of-the-art top-1 accuracy on ImageNet of 90.88%.

Comparison between CoAtNets and previous ViTs. ImageNet top-1 accuracy after pre-training on JFT dataset under different training budget. The four best models are trained on JFT-3B with about 3 billion images.

Conclusion and Future Work
In this post, we introduce two families of neural networks, named EfficientNetV2 and CoAtNet, which achieve state-of-the-art performance on image recognition. All EfficientNetV2 models are open sourced and the pretrained models are also available on the TFhub. CoAtNet models will also be open-sourced soon. We hope these new neural networks can benefit the research community and the industry. In the future we plan to further optimize these models and apply them to new tasks, such as zero-shot learning and self-supervised learning, which often require fast models with high capacity.

Acknowledgements
Special thanks to our co-authors Hanxiao Liu and Quoc Le. We also thank the Google Research, Brain Team and the open source contributors.

Source: Google AI Blog


Evolving Reinforcement Learning Algorithms

A long-term, overarching goal of research into reinforcement learning (RL) is to design a single general purpose learning algorithm that can solve a wide array of problems. However, because the RL algorithm taxonomy is quite large, and designing new RL algorithms requires extensive tuning and validation, this goal is a daunting one. A possible solution would be to devise a meta-learning method that could design new RL algorithms that generalize to a wide variety of tasks automatically.

In recent years, AutoML has shown great success in automating the design of machine learning components, such as neural networks architectures and model update rules. One example is Neural Architecture Search (NAS), which has been used to develop better neural network architectures for image classification and efficient architectures for running on phones and hardware accelerators. In addition to NAS, AutoML-Zero shows that it’s even possible to learn the entire algorithm from scratch using basic mathematical operations. One common theme in these approaches is that the neural network architecture or the entire algorithm is represented by a graph, and a separate algorithm is used to optimize the graph for certain objectives.

These earlier approaches were designed for supervised learning, in which the overall algorithm is more straightforward. But in RL, there are more components of the algorithm that could be potential targets for design automation (e.g., neural network architectures for agent networks, strategies for sampling from the replay buffer, overall formulation of the loss function), and it is not always clear what the best model update procedure would be to integrate these components. Prior efforts for the automation RL algorithm discovery have focused primarily on model update rules. These approaches learn the optimizer or RL update procedure itself and commonly represent the update rule with a neural network such as an RNN or CNN, which can be efficiently optimized with gradient-based methods. However, these learned rules are not interpretable or generalizable, because the learned weights are opaque and domain specific.

In our paper “Evolving Reinforcement Learning Algorithms”, accepted at ICLR 2021, we show that it’s possible to learn new, analytically interpretable and generalizable RL algorithms by using a graph representation and applying optimization techniques from the AutoML community. In particular, we represent the loss function, which is used to optimize an agent’s parameters over its experience, as a computational graph, and use Regularized Evolution to evolve a population of the computational graphs over a set of simple training environments. This results in increasingly better RL algorithms, and the discovered algorithms generalize to more complex environments, even those with visual observations like Atari games.

RL Algorithm as a Computational Graph
Inspired by ideas from NAS, which searches over the space of graphs representing neural network architectures, we meta-learn RL algorithms by representing the loss function of an RL algorithm as a computational graph. In this case, we use a directed acyclic graph for the loss function, with nodes representing inputs, operators, parameters and outputs. For example, in the computational graph for DQN, input nodes include data from the replay buffer, operator nodes include neural network operators and basic math operators, and the output node represents the loss, which will be minimized with gradient descent.

There are a few benefits of such a representation. This representation is expressive enough to define existing algorithms but also new, undiscovered algorithms. It is also interpretable. This graph representation can be analyzed in the same way as human designed RL algorithms, making it more interpretable than approaches that use black box function approximators for the entire RL update procedure. If researchers can understand why a learned algorithm is better, then they can both modify the internal components of the algorithm to improve it and transfer the beneficial components to other problems. Finally, the representation supports general algorithms that can solve a wide variety of problems.

Example computation graph for DQN which computes the squared Bellman error.

We implemented this representation using the PyGlove library, which conveniently turns the graph into a search space that can be optimized with regularized evolution.

Evolving RL Algorithms
We use an evolutionary based approach to optimize the RL algorithms of interest. First, we initialize a population of training agents with randomized graphs. This population of agents is trained in parallel over a set of training environments. The agents first train on a hurdle environment — an easy environment, such as CartPole, intended to quickly weed out poorly performing programs.

If an agent cannot solve the hurdle environment, the training is stopped early with a score of zero. Otherwise the training proceeds to more difficult environments (e.g., Lunar Lander, simple MiniGrid environments, etc.). The algorithm performance is evaluated and used to update the population, where more promising algorithms are further mutated. To reduce the search space, we use a functional equivalence checker which will skip over newly proposed algorithms if they are functionally the same as previously examined algorithms. This loop continues as new mutated candidate algorithms are trained and evaluated. At the end of training, we select the best algorithm and evaluate its performance over a set of unseen test environments.

The population size in the experiments was around 300 agents, and we observed the evolution of good candidate loss functions after 20-50 thousand mutations, requiring about three days of training. We were able to train on CPUs because the training environments were simple, controlling for the computational and energy cost of training. To further control the cost of training, we seeded the initial population with human-designed RL algorithms such as DQN.

Overview of meta-learning method. Newly proposed algorithms must first perform well on a hurdle environment before being trained on a set of harder environments. Algorithm performance is used to update a population where better performing algorithms are further mutated into new algorithms. At the end of training, the best performing algorithm is evaluated on test environments.

Learned Algorithms
We highlight two discovered algorithms that exhibit good generalization performance. The first is DQNReg, which builds on DQN by adding a weighted penalty on the Q-values to the normal squared Bellman error. The second learned loss function, DQNClipped, is more complex, although its dominating term has a simple form — the max of the Q-value and the squared Bellman error (modulo a constant). Both algorithms can be viewed as a way to regularize the Q-values. While DQNReg adds a soft constraint, DQNClipped can be interpreted as a kind of constrained optimization that will minimize the Q-values if they become too large. We show that this learned constraint kicks in during the early stage of training when overestimating the Q-values is a potential issue. Once this constraint is satisfied, the loss will instead minimize the original squared Bellman error.

A closer analysis shows that while baselines like DQN commonly overestimate Q-values, our learned algorithms address this issue in different ways. DQNReg underestimates the Q-values, while DQNClipped has similar behavior to double dqn in that it slowly approaches the ground truth without overestimating it.

It’s worth pointing out that these two algorithms consistently emerge when the evolution is seeded with DQN. Learning from scratch, the method rediscovers the TD algorithm. For completeness, we release a dataset of top 1000 performing algorithms discovered during evolution. Curious readers could further investigate the properties of these learned loss functions.

Overestimated values are generally a problem in value-based RL. Our method learns algorithms that have found a way to regularize the Q-values and thus reduce overestimation.

Learned Algorithms Generalization Performance
Normally in RL, generalization refers to a trained policy generalizing across tasks. However, in this work we’re interested in algorithmic generalization performance, which means how well an algorithm works over a set of environments. On a set of classical control environments, the learned algorithms can match baselines on the dense reward tasks (CartPole, Acrobot, LunarLander) and outperform DQN on the sparser reward task, MountainCar.

Performance of learned algorithms versus baselines on classical control environments.

On a set of sparse reward MiniGrid environments, which test a variety of different tasks, we see that DQNReg greatly outperforms baselines on both the training and test environments, in terms of sample efficiency and final performance. In fact, the effect is even more pronounced on the test environments, which vary in size, configuration, and existence of new obstacles, such as lava.

Training environment performance versus training steps as measured by episode return over 10 training seeds. DQNReg can match or outperform baselines in sample efficiency and final performance.
DQNReg can greatly outperform baselines on unseen test environments.

We visualize the performance of normal DDQN vs. the learned algorithm DQNReg on a few MiniGrid environments. The starting location, wall configuration, and object configuration of these environments are randomized at each reset, which requires the agent to generalize instead of simply memorizing the environment. While DDQN often struggles to learn any meaningful behavior, DQNReg can learn the optimal behavior efficiently.

DDQN
DQNReg (Learned) 

Even on image-based Atari environments we observe improved performance, even though training was on non-image-based environments. This suggests that meta-training on a set of cheap but diverse training environments with a generalizable algorithm representation could enable radical algorithmic generalization.

EnvDQNDDQNPPODQNReg
Asteroid1364.5734.72097.52390.4
Bowling50.468.140.180.5
Boxing88.091.694.6100.0
RoadRunner  39544.0    44127.0    35466.0    65516.0  
Performance of learned algorithm, DQNReg, against baselines on several Atari games. Performance is evaluated over 200 test episodes every 1 million steps.

Conclusion
In this post, we’ve discussed learning new interpretable RL algorithms by representing their loss functions as computational graphs and evolving a population of agents over this representation. The computational graph formulation allows researchers to both build upon human-designed algorithms and study the learned algorithms using the same mathematical toolset as the existing algorithms. We analyzed a few of the learned algorithms and can interpret them as a form of entropy regularization to prevent value overestimation. These learned algorithms can outperform baselines and generalize to unseen environments. The top performing algorithms are available for further analytical study.

We hope that future work will extend to more varied RL settings such as actor critic algorithms or offline RL. Furthermore we hope that this work can lead to machine assisted algorithm development where computational meta-learning can help researchers find new directions to pursue and incorporate learned algorithms into their own work.

Acknowledgements
We thank our co-authors Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, and Aleksandra Faust. We also thank Luke Metz for helpful early discussions and feedback on the paper, Hanjun Dai for early discussions on related research ideas, Xingyou Song, Krzysztof Choromanski, and Kevin Wu for helping with infrastructure, and Jongwook Choi for helping with environment selection. Finally we thank Tom Small for designing animations for this post.

Source: Google AI Blog


Introducing Model Search: An Open Source Platform for Finding Optimal ML Models

The success of a neural network (NN) often depends on how well it can generalize to various tasks. However, designing NNs that can generalize well is challenging because the research community's understanding of how a neural network generalizes is currently somewhat limited: What does the appropriate neural network look like for a given problem? How deep should it be? Which types of layers should be used? Would LSTMs be enough or would Transformer layers be better? Or maybe a combination of the two? Would ensembling or distillation boost performance? These tricky questions are made even more challenging when considering machine learning (ML) domains where there may exist better intuition and deeper understanding than others.

In recent years, AutoML algorithms have emerged [e.g., 1, 2, 3] to help researchers find the right neural network automatically without the need for manual experimentation. Techniques like neural architecture search (NAS), use algorithms, like reinforcement learning (RL), evolutionary algorithms, and combinatorial search, to build a neural network out of a given search space. With the proper setup, these techniques have demonstrated they are capable of delivering results that are better than the manually designed counterparts. But more often than not, these algorithms are compute heavy, and need thousands of models to train before converging. Moreover, they explore search spaces that are domain specific and incorporate substantial prior human knowledge that does not transfer well across domains. As an example, in image classification, the traditional NAS searches for two good building blocks (convolutional and downsampling blocks), that it arranges following traditional conventions to create the full network.

To overcome these shortcomings and to extend access to AutoML solutions to the broader research community, we are excited to announce the open source release of Model Search, a platform that helps researchers develop the best ML models, efficiently and automatically. Instead of focusing on a specific domain, Model Search is domain agnostic, flexible and is capable of finding the appropriate architecture that best fits a given dataset and problem, while minimizing coding time, effort and compute resources. It is built on Tensorflow, and can run either on a single machine or in a distributed setting.

Overview
The Model Search system consists of multiple trainers, a search algorithm, a transfer learning algorithm and a database to store the various evaluated models. The system runs both training and evaluation experiments for various ML models (different architectures and training techniques) in an adaptive, yet asynchronous, fashion. While each trainer conducts experiments independently, all trainers share the knowledge gained from their experiments. At the beginning of every cycle, the search algorithm looks up all the completed trials and uses beam search to decide what to try next. It then invokes mutation over one of the best architectures found thus far and assigns the resulting model back to a trainer.

Model Search schematic illustrating the distributed search and ensembling. Each trainer runs independently to train and evaluate a given model. The results are shared with the search algorithm, which it stores. The search algorithm then invokes mutation over one of the best architectures and then sends the new model back to a trainer for the next iteration. S is the set of training and validation examples and A are all the candidates used during training and search.

The system builds a neural network model from a set of predefined blocks, each of which represents a known micro-architecture, like LSTM, ResNet or Transformer layers. By using blocks of pre-existing architectural components, Model Search is able to leverage existing best knowledge from NAS research across domains. This approach is also more efficient, because it explores structures, not their more fundamental and detailed components, therefore reducing the scale of the search space.

Neural network micro architecture blocks that work well, e.g., a ResNet Block.

Because the Model Search framework is built on Tensorflow, blocks can implement any function that takes a tensor as an input. For example, imagine that one wants to introduce a new search space built with a selection of micro architectures. The framework will take the newly defined blocks and incorporate them into the search process so that algorithms can build the best possible neural network from the components provided. The blocks provided can even be fully defined neural networks that are already known to work for the problem of interest. In that case, Model Search can be configured to simply act as a powerful ensembling machine.

The search algorithms implemented in Model Search are adaptive, greedy and incremental, which makes them converge faster than RL algorithms. They do however imitate the “explore & exploit” nature of RL algorithms by separating the search for a good candidate (explore step), and boosting accuracy by ensembling good candidates that were discovered (exploit step). The main search algorithm adaptively modifies one of the top k performing experiments (where k can be specified by the user) after applying random changes to the architecture or the training technique (e.g., making the architecture deeper).

An example of an evolution of a network over many experiments. Each color represents a different type of architecture block. The final network is formed via mutations of high performing candidate networks, in this case adding depth.

To further improve efficiency and accuracy, transfer learning is enabled between various internal experiments. Model Search does this in two ways — via knowledge distillation or weight sharing. Knowledge distillation allows one to improve candidates' accuracies by adding a loss term that matches the high performing models’ predictions in addition to the ground truth. Weight sharing, on the other hand, bootstraps some of the parameters (after applying mutation) in the network from previously trained candidates by copying suitable weights from previously trained models and randomly initializing the remaining ones. This enables faster training, which allows opportunities to discover more (and better) architectures.

Experimental Results
Model Search improves upon production models with minimal iterations. In a recent paper, we demonstrated the capabilities of Model Search in the speech domain by discovering a model for keyword spotting and language identification. Over fewer than 200 iterations, the resulting model slightly improved upon internal state-of-the-art production models designed by experts in accuracy using ~130K fewer trainable parameters (184K compared to 315K parameters).

Model accuracy given iteration in our system compared to the previous production model for keyword spotting, a similar graph can be found for language identification in the linked paper.

We also applied Model Search to find an architecture suitable for image classification on the heavily explored CIFAR-10 imaging dataset. Using a set known convolution blocks, including convolutions, resnet blocks (i.e., two convolutions and a skip connection), NAS-A cells, fully connected layers, etc., we observed that we were able to quickly reach a benchmark accuracy of 91.83 in 209 trials (i.e., exploring only 209 models). In comparison, previous top performers reached the same threshold accuracy in 5807 trials for the NasNet algorithm (RL), and 1160 for PNAS (RL + Progressive).

Conclusion
We hope the Model Search code will provide researchers with a flexible, domain-agnostic framework for ML model discovery. By building upon previous knowledge for a given domain, we believe that this framework is powerful enough to build models with the state-of-the-art performance on well studied problems when provided with a search space composed of standard building blocks.

Acknowledgements
Special thanks to all code contributors to the open sourcing and the paper: Eugen Ehotaj, Scotty Yak, Malaika Handa, James Preiss, Pai Zhu, Aleks Kracun, Prashant Sridhar, Niranjan Subrahmanya, Ignacio Lopez Moreno, Hyun Jin Park, and Patrick Violette.

Source: Google AI Blog


Introducing Model Search: An Open Source Platform for Finding Optimal ML Models

The success of a neural network (NN) often depends on how well it can generalize to various tasks. However, designing NNs that can generalize well is challenging because the research community's understanding of how a neural network generalizes is currently somewhat limited: What does the appropriate neural network look like for a given problem? How deep should it be? Which types of layers should be used? Would LSTMs be enough or would Transformer layers be better? Or maybe a combination of the two? Would ensembling or distillation boost performance? These tricky questions are made even more challenging when considering machine learning (ML) domains where there may exist better intuition and deeper understanding than others.

In recent years, AutoML algorithms have emerged [e.g., 1, 2, 3] to help researchers find the right neural network automatically without the need for manual experimentation. Techniques like neural architecture search (NAS), use algorithms, like reinforcement learning (RL), evolutionary algorithms, and combinatorial search, to build a neural network out of a given search space. With the proper setup, these techniques have demonstrated they are capable of delivering results that are better than the manually designed counterparts. But more often than not, these algorithms are compute heavy, and need thousands of models to train before converging. Moreover, they explore search spaces that are domain specific and incorporate substantial prior human knowledge that does not transfer well across domains. As an example, in image classification, the traditional NAS searches for two good building blocks (convolutional and downsampling blocks), that it arranges following traditional conventions to create the full network.

To overcome these shortcomings and to extend access to AutoML solutions to the broader research community, we are excited to announce the open source release of Model Search, a platform that helps researchers develop the best ML models, efficiently and automatically. Instead of focusing on a specific domain, Model Search is domain agnostic, flexible and is capable of finding the appropriate architecture that best fits a given dataset and problem, while minimizing coding time, effort and compute resources. It is built on Tensorflow, and can run either on a single machine or in a distributed setting.

Overview
The Model Search system consists of multiple trainers, a search algorithm, a transfer learning algorithm and a database to store the various evaluated models. The system runs both training and evaluation experiments for various ML models (different architectures and training techniques) in an adaptive, yet asynchronous, fashion. While each trainer conducts experiments independently, all trainers share the knowledge gained from their experiments. At the beginning of every cycle, the search algorithm looks up all the completed trials and uses beam search to decide what to try next. It then invokes mutation over one of the best architectures found thus far and assigns the resulting model back to a trainer.

Model Search schematic illustrating the distributed search and ensembling. Each trainer runs independently to train and evaluate a given model. The results are shared with the search algorithm, which it stores. The search algorithm then invokes mutation over one of the best architectures and then sends the new model back to a trainer for the next iteration. S is the set of training and validation examples and A are all the candidates used during training and search.

The system builds a neural network model from a set of predefined blocks, each of which represents a known micro-architecture, like LSTM, ResNet or Transformer layers. By using blocks of pre-existing architectural components, Model Search is able to leverage existing best knowledge from NAS research across domains. This approach is also more efficient, because it explores structures, not their more fundamental and detailed components, therefore reducing the scale of the search space.

Neural network micro architecture blocks that work well, e.g., a ResNet Block.

Because the Model Search framework is built on Tensorflow, blocks can implement any function that takes a tensor as an input. For example, imagine that one wants to introduce a new search space built with a selection of micro architectures. The framework will take the newly defined blocks and incorporate them into the search process so that algorithms can build the best possible neural network from the components provided. The blocks provided can even be fully defined neural networks that are already known to work for the problem of interest. In that case, Model Search can be configured to simply act as a powerful ensembling machine.

The search algorithms implemented in Model Search are adaptive, greedy and incremental, which makes them converge faster than RL algorithms. They do however imitate the “explore & exploit” nature of RL algorithms by separating the search for a good candidate (explore step), and boosting accuracy by ensembling good candidates that were discovered (exploit step). The main search algorithm adaptively modifies one of the top k performing experiments (where k can be specified by the user) after applying random changes to the architecture or the training technique (e.g., making the architecture deeper).

An example of an evolution of a network over many experiments. Each color represents a different type of architecture block. The final network is formed via mutations of high performing candidate networks, in this case adding depth.

To further improve efficiency and accuracy, transfer learning is enabled between various internal experiments. Model Search does this in two ways — via knowledge distillation or weight sharing. Knowledge distillation allows one to improve candidates' accuracies by adding a loss term that matches the high performing models’ predictions in addition to the ground truth. Weight sharing, on the other hand, bootstraps some of the parameters (after applying mutation) in the network from previously trained candidates by copying suitable weights from previously trained models and randomly initializing the remaining ones. This enables faster training, which allows opportunities to discover more (and better) architectures.

Experimental Results
Model Search improves upon production models with minimal iterations. In a recent paper, we demonstrated the capabilities of Model Search in the speech domain by discovering a model for keyword spotting and language identification. Over fewer than 200 iterations, the resulting model slightly improved upon internal state-of-the-art production models designed by experts in accuracy using ~130K fewer trainable parameters (184K compared to 315K parameters).

Model accuracy given iteration in our system compared to the previous production model for keyword spotting, a similar graph can be found for language identification in the linked paper.

We also applied Model Search to find an architecture suitable for image classification on the heavily explored CIFAR-10 imaging dataset. Using a set known convolution blocks, including convolutions, resnet blocks (i.e., two convolutions and a skip connection), NAS-A cells, fully connected layers, etc., we observed that we were able to quickly reach a benchmark accuracy of 91.83 in 209 trials (i.e., exploring only 209 models). In comparison, previous top performers reached the same threshold accuracy in 5807 trials for the NasNet algorithm (RL), and 1160 for PNAS (RL + Progressive).

Conclusion
We hope the Model Search code will provide researchers with a flexible, domain-agnostic framework for ML model discovery. By building upon previous knowledge for a given domain, we believe that this framework is powerful enough to build models with the state-of-the-art performance on well studied problems when provided with a search space composed of standard building blocks.

Acknowledgements
Special thanks to all code contributors to the open sourcing and the paper: Eugen Ehotaj, Scotty Yak, Malaika Handa, James Preiss, Pai Zhu, Aleks Kracun, Prashant Sridhar, Niranjan Subrahmanya, Ignacio Lopez Moreno, Hyun Jin Park, and Patrick Violette.

Source: Google AI Blog


Introducing Model Search: An Open Source Platform for Finding Optimal ML Models

The success of a neural network (NN) often depends on how well it can generalize to various tasks. However, designing NNs that can generalize well is challenging because the research community's understanding of how a neural network generalizes is currently somewhat limited: What does the appropriate neural network look like for a given problem? How deep should it be? Which types of layers should be used? Would LSTMs be enough or would Transformer layers be better? Or maybe a combination of the two? Would ensembling or distillation boost performance? These tricky questions are made even more challenging when considering machine learning (ML) domains where there may exist better intuition and deeper understanding than others.

In recent years, AutoML algorithms have emerged [e.g., 1, 2, 3] to help researchers find the right neural network automatically without the need for manual experimentation. Techniques like neural architecture search (NAS), use algorithms, like reinforcement learning (RL), evolutionary algorithms, and combinatorial search, to build a neural network out of a given search space. With the proper setup, these techniques have demonstrated they are capable of delivering results that are better than the manually designed counterparts. But more often than not, these algorithms are compute heavy, and need thousands of models to train before converging. Moreover, they explore search spaces that are domain specific and incorporate substantial prior human knowledge that does not transfer well across domains. As an example, in image classification, the traditional NAS searches for two good building blocks (convolutional and downsampling blocks), that it arranges following traditional conventions to create the full network.

To overcome these shortcomings and to extend access to AutoML solutions to the broader research community, we are excited to announce the open source release of Model Search, a platform that helps researchers develop the best ML models, efficiently and automatically. Instead of focusing on a specific domain, Model Search is domain agnostic, flexible and is capable of finding the appropriate architecture that best fits a given dataset and problem, while minimizing coding time, effort and compute resources. It is built on Tensorflow, and can run either on a single machine or in a distributed setting.

Overview
The Model Search system consists of multiple trainers, a search algorithm, a transfer learning algorithm and a database to store the various evaluated models. The system runs both training and evaluation experiments for various ML models (different architectures and training techniques) in an adaptive, yet asynchronous, fashion. While each trainer conducts experiments independently, all trainers share the knowledge gained from their experiments. At the beginning of every cycle, the search algorithm looks up all the completed trials and uses beam search to decide what to try next. It then invokes mutation over one of the best architectures found thus far and assigns the resulting model back to a trainer.

Model Search schematic illustrating the distributed search and ensembling. Each trainer runs independently to train and evaluate a given model. The results are shared with the search algorithm, which it stores. The search algorithm then invokes mutation over one of the best architectures and then sends the new model back to a trainer for the next iteration. S is the set of training and validation examples and A are all the candidates used during training and search.

The system builds a neural network model from a set of predefined blocks, each of which represents a known micro-architecture, like LSTM, ResNet or Transformer layers. By using blocks of pre-existing architectural components, Model Search is able to leverage existing best knowledge from NAS research across domains. This approach is also more efficient, because it explores structures, not their more fundamental and detailed components, therefore reducing the scale of the search space.

Neural network micro architecture blocks that work well, e.g., a ResNet Block.

Because the Model Search framework is built on Tensorflow, blocks can implement any function that takes a tensor as an input. For example, imagine that one wants to introduce a new search space built with a selection of micro architectures. The framework will take the newly defined blocks and incorporate them into the search process so that algorithms can build the best possible neural network from the components provided. The blocks provided can even be fully defined neural networks that are already known to work for the problem of interest. In that case, Model Search can be configured to simply act as a powerful ensembling machine.

The search algorithms implemented in Model Search are adaptive, greedy and incremental, which makes them converge faster than RL algorithms. They do however imitate the “explore & exploit” nature of RL algorithms by separating the search for a good candidate (explore step), and boosting accuracy by ensembling good candidates that were discovered (exploit step). The main search algorithm adaptively modifies one of the top k performing experiments (where k can be specified by the user) after applying random changes to the architecture or the training technique (e.g., making the architecture deeper).

An example of an evolution of a network over many experiments. Each color represents a different type of architecture block. The final network is formed via mutations of high performing candidate networks, in this case adding depth.

To further improve efficiency and accuracy, transfer learning is enabled between various internal experiments. Model Search does this in two ways — via knowledge distillation or weight sharing. Knowledge distillation allows one to improve candidates' accuracies by adding a loss term that matches the high performing models’ predictions in addition to the ground truth. Weight sharing, on the other hand, bootstraps some of the parameters (after applying mutation) in the network from previously trained candidates by copying suitable weights from previously trained models and randomly initializing the remaining ones. This enables faster training, which allows opportunities to discover more (and better) architectures.

Experimental Results
Model Search improves upon production models with minimal iterations. In a recent paper, we demonstrated the capabilities of Model Search in the speech domain by discovering a model for keyword spotting and language identification. Over fewer than 200 iterations, the resulting model slightly improved upon internal state-of-the-art production models designed by experts in accuracy using ~130K fewer trainable parameters (184K compared to 315K parameters).

Model accuracy given iteration in our system compared to the previous production model for keyword spotting, a similar graph can be found for language identification in the linked paper.

We also applied Model Search to find an architecture suitable for image classification on the heavily explored CIFAR-10 imaging dataset. Using a set known convolution blocks, including convolutions, resnet blocks (i.e., two convolutions and a skip connection), NAS-A cells, fully connected layers, etc., we observed that we were able to quickly reach a benchmark accuracy of 91.83 in 209 trials (i.e., exploring only 209 models). In comparison, previous top performers reached the same threshold accuracy in 5807 trials for the NasNet algorithm (RL), and 1160 for PNAS (RL + Progressive).

Conclusion
We hope the Model Search code will provide researchers with a flexible, domain-agnostic framework for ML model discovery. By building upon previous knowledge for a given domain, we believe that this framework is powerful enough to build models with the state-of-the-art performance on well studied problems when provided with a search space composed of standard building blocks.

Acknowledgements
Special thanks to all code contributors to the open sourcing and the paper: Eugen Ehotaj, Scotty Yak, Malaika Handa, James Preiss, Pai Zhu, Aleks Kracun, Prashant Sridhar, Niranjan Subrahmanya, Ignacio Lopez Moreno, Hyun Jin Park, and Patrick Violette.

Source: Google AI Blog


Releasing the Healthcare Text Annotation Guidelines

The Healthcare Text Annotation Guidelines are blueprints for capturing a structured representation of the medical knowledge stored in digital text. In order to automatically map the textual insights to structured knowledge, the annotations generated using these guidelines are fed into a machine learning algorithm that learns to systematically extract the medical knowledge in the text. We’re pleased to release to the public the Healthcare Text Annotation Guidelines as a standard.

Google Cloud recently launched AutoML Entity Extraction for Healthcare, a low-code tool used to build information extraction models for healthcare applications. There remains a significant execution roadblock on AutoML DIY initiatives caused by the complexity of translating the human cognitive process into machine-readable instructions. Today, this translation occurs thanks to human annotators who annotate text for relevant insights. Yet, training human annotators is a complex endeavor which requires knowledge across fields like linguistics and neuroscience, as well as a good understanding of the business domain. With AutoML, Google wanted to democratize who can build AI. The Healthcare Text Annotation Guidelines are a starting point for annotation projects deployed for healthcare applications.

The guidelines provide a reference for training annotators in addition to explicit blueprints for several healthcare annotation tasks. The annotation guidelines cover the following:
  • The task of medical entity extraction with examples from medical entity types like medications, procedures, and body vitals.
  • Additional tasks with defined examples, such as entity relation annotation and entity attribute annotation. For instance, the guidelines specify how to relate a medical procedure entity to the source medical condition entity, or how to capture the attributes of a medication entity like dosage, frequency, and route of administration.
  • Guidance for annotating an entity’s contextual information like temporal assessment (e.g., current, family history, clinical history), certainty assessment (e.g., unlikely, somewhat likely, likely), and subject (e.g., patient, family member, other).
Google consulted with industry experts and academic institutions in the process of assembling the Healthcare Text Annotation Guidelines. We took inspiration from other open source and research projects like i2b2 and added context to the guidelines to support information extraction needs for industry-applications like Healthcare Effectiveness Data and Information Set (HEDIS) quality reporting. The data types contained in the Healthcare Text Annotation Guidelines are a common denominator across information extraction applications. Each industry application can have additional information extraction needs that are not captured in the current version of the guidelines. We chose to open source this asset so the community can tailor this project to their needs.

We’re thrilled to open source this project. We hope the community will contribute to the refinement and expansion of the Healthcare Text Annotation Guidelines, so they mirror the ever-evolving nature of healthcare.

By Andreea Bodnari, Product Manager and Mikhail Begun, Program Manager—Google Cloud AI

Releasing the Healthcare Text Annotation Guidelines

The Healthcare Text Annotation Guidelines are blueprints for capturing a structured representation of the medical knowledge stored in digital text. In order to automatically map the textual insights to structured knowledge, the annotations generated using these guidelines are fed into a machine learning algorithm that learns to systematically extract the medical knowledge in the text. We’re pleased to release to the public the Healthcare Text Annotation Guidelines as a standard.

Google Cloud recently launched AutoML Entity Extraction for Healthcare, a low-code tool used to build information extraction models for healthcare applications. There remains a significant execution roadblock on AutoML DIY initiatives caused by the complexity of translating the human cognitive process into machine-readable instructions. Today, this translation occurs thanks to human annotators who annotate text for relevant insights. Yet, training human annotators is a complex endeavor which requires knowledge across fields like linguistics and neuroscience, as well as a good understanding of the business domain. With AutoML, Google wanted to democratize who can build AI. The Healthcare Text Annotation Guidelines are a starting point for annotation projects deployed for healthcare applications.

The guidelines provide a reference for training annotators in addition to explicit blueprints for several healthcare annotation tasks. The annotation guidelines cover the following:
  • The task of medical entity extraction with examples from medical entity types like medications, procedures, and body vitals.
  • Additional tasks with defined examples, such as entity relation annotation and entity attribute annotation. For instance, the guidelines specify how to relate a medical procedure entity to the source medical condition entity, or how to capture the attributes of a medication entity like dosage, frequency, and route of administration.
  • Guidance for annotating an entity’s contextual information like temporal assessment (e.g., current, family history, clinical history), certainty assessment (e.g., unlikely, somewhat likely, likely), and subject (e.g., patient, family member, other).
Google consulted with industry experts and academic institutions in the process of assembling the Healthcare Text Annotation Guidelines. We took inspiration from other open source and research projects like i2b2 and added context to the guidelines to support information extraction needs for industry-applications like Healthcare Effectiveness Data and Information Set (HEDIS) quality reporting. The data types contained in the Healthcare Text Annotation Guidelines are a common denominator across information extraction applications. Each industry application can have additional information extraction needs that are not captured in the current version of the guidelines. We chose to open source this asset so the community can tailor this project to their needs.

We’re thrilled to open source this project. We hope the community will contribute to the refinement and expansion of the Healthcare Text Annotation Guidelines, so they mirror the ever-evolving nature of healthcare.

By Andreea Bodnari, Product Manager and Mikhail Begun, Program Manager—Google Cloud AI

Doubling down on the edge with Coral’s new accelerator

Posted by The Coral Team

Coral image

Moving into the fall, the Coral platform continues to grow with the release of the M.2 Accelerator with Dual Edge TPU. Its first application is in Google’s Series One room kits where it helps to remove interruptions and makes the audio clearer for better video meetings. To help even more folks build products with Coral intelligence, we’re dropping the prices on several of our products. And for those folks that are looking to level up their at home video production, we’re sharing a demo of a pose based AI director to make multi-camera video easier to make.

Coral M.2 Accelerator with Dual Edge TPU

The newest addition to our product family brings two Edge TPU co-processors to systems in an M.2 E-key form factor. While the design requires a dual bus PCIe M.2 slot, it brings enhanced ML performance (8 TOPS) to tasks such as running two models in parallel or pipelining one large model across both Edge TPUs.

The ability to scale across multiple edge accelerators isn’t limited to only two Edge TPUs. As edge computing expands to local data centers, cell towers, and gateways, multi-Edge TPU configurations will be required to help process increasingly sophisticated ML models. Coral allows the use of a single toolchain to create models for one or more Edge TPUs that can address many different future configurations.

A great example of how the Coral M.2 Accelerator with Dual Edge TPU is being used is in the Series One meeting room kits for Google Meet.

The new Series One room kits for Google Meet run smarter with Coral intelligence

Coral image

Google’s new Series One room kits use our Coral M.2 Accelerator with Dual Edge TPU to bring enhanced audio clarity to video meetings. TrueVoice®, a multi-channel noise cancellation technology, minimizes distractions to ensure every voice is heard with up to 44 channels of echo and noise cancellation, making distracting sounds like snacking or typing on a keyboard a concern of the past.

Enabling the clearest possible communication in challenging environments was the target for the Google Meet hardware team. The consideration of what makes a challenging environment was not limited to unusually noisy environments, such as lunchrooms doubling as conference rooms. Any conference room can present challenging acoustics that make it difficult for all participants to be heard.

The secret to clarity without expensive and cumbersome equipment is to use virtual audio channels and AI driven sound isolation. Read more about how Coral was used to enhance and future-proof the innovative design.

Expanding the AI edge

Earlier this year, we reduced the prices of our prototyping devices and sensors. We are excited to share further price drops on more of our products. Our System-on-Module is now available for $99.99, and our Mini PCIe Accelerator, M.2 Accelerator A+E Key, and M.2 Accelerator B+M key are now available at $24.99. We hope this lower price will make our edge AI more accessible to more creative minds around the world. Later, this month our SoM offering will also expand to include 2 and 4GB RAM options.

Multi-cam with AI

Coral image

As we expand our platform and product family, we continue to keep new edge AI use cases in mind. We are continually inspired by our developer community’s experimentation and implementations. When recently faced with the challenges of multicam video production from home, Markku Lepistö, Solutions Architect at Google Cloud, created this real-time pose-based multicam tool he so aptly dubbed, AI Director.

We love seeing such unique implementations of on-device ML and invite you to share your own projects and feedback at [email protected].

For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform.

Doubling down on the edge with Coral’s new accelerator

Posted by The Coral Team

Coral image

Moving into the fall, the Coral platform continues to grow with the release of the M.2 Accelerator with Dual Edge TPU. Its first application is in Google’s Series One room kits where it helps to remove interruptions and makes the audio clearer for better video meetings. To help even more folks build products with Coral intelligence, we’re dropping the prices on several of our products. And for those folks that are looking to level up their at home video production, we’re sharing a demo of a pose based AI director to make multi-camera video easier to make.

Coral M.2 Accelerator with Dual Edge TPU

The newest addition to our product family brings two Edge TPU co-processors to systems in an M.2 E-key form factor. While the design requires a dual bus PCIe M.2 slot, it brings enhanced ML performance (8 TOPS) to tasks such as running two models in parallel or pipelining one large model across both Edge TPUs.

The ability to scale across multiple edge accelerators isn’t limited to only two Edge TPUs. As edge computing expands to local data centers, cell towers, and gateways, multi-Edge TPU configurations will be required to help process increasingly sophisticated ML models. Coral allows the use of a single toolchain to create models for one or more Edge TPUs that can address many different future configurations.

A great example of how the Coral M.2 Accelerator with Dual Edge TPU is being used is in the Series One meeting room kits for Google Meet.

The new Series One room kits for Google Meet run smarter with Coral intelligence

Coral image

Google’s new Series One room kits use our Coral M.2 Accelerator with Dual Edge TPU to bring enhanced audio clarity to video meetings. TrueVoice®, a multi-channel noise cancellation technology, minimizes distractions to ensure every voice is heard with up to 44 channels of echo and noise cancellation, making distracting sounds like snacking or typing on a keyboard a concern of the past.

Enabling the clearest possible communication in challenging environments was the target for the Google Meet hardware team. The consideration of what makes a challenging environment was not limited to unusually noisy environments, such as lunchrooms doubling as conference rooms. Any conference room can present challenging acoustics that make it difficult for all participants to be heard.

The secret to clarity without expensive and cumbersome equipment is to use virtual audio channels and AI driven sound isolation. Read more about how Coral was used to enhance and future-proof the innovative design.

Expanding the AI edge

Earlier this year, we reduced the prices of our prototyping devices and sensors. We are excited to share further price drops on more of our products. Our System-on-Module is now available for $99.99, and our Mini PCIe Accelerator, M.2 Accelerator A+E Key, and M.2 Accelerator B+M key are now available at $24.99. We hope this lower price will make our edge AI more accessible to more creative minds around the world. Later, this month our SoM offering will also expand to include 2 and 4GB RAM options.

Multi-cam with AI

Coral image

As we expand our platform and product family, we continue to keep new edge AI use cases in mind. We are continually inspired by our developer community’s experimentation and implementations. When recently faced with the challenges of multicam video production from home, Markku Lepistö, Solutions Architect at Google Cloud, created this real-time pose-based multicam tool he so aptly dubbed, AI Director.

We love seeing such unique implementations of on-device ML and invite you to share your own projects and feedback at [email protected].

For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform.