Tag Archives: IoT

New Coral products for 2020

Posted by Billy Rutledge, Director Google Research, Coral Team

More and more industries are beginning to recognize the value of local AI, where the speed of local inference allows considerable savings on bandwidth and cloud compute costs, and keeping data local preserves user privacy.

Last year, we launched Coral, our platform of hardware components and software tools that make it easy to prototype and scale local AI products. Our product portfolio includes the Coral Dev Board, USB Accelerator, and PCIe Accelerators, all now available in 36 countries.

Since our release, we’ve been excited by the diverse range of applications already built on Coral across a broad set of industries that range from healthcare to agriculture to smart cities. And for 2020, we’re excited to announce new additions to the Coral platform that will expand the possibilities even further.

First up is the Coral Accelerator Module, an easy to integrate multi-chip package that encapsulates the Edge TPU ASIC. The module exposes both PCIe and USB interfaces and can easily integrate into custom PCB designs. We’ve been working closely with Murata to produce the module and you can see a demo at CES 2020 by visiting their booth at the Las Vegas Convention Center, Tech East, Central Plaza, CP-18. The Coral Accelerator Module will be available in the first half of 2020.

Coral Accelerator Module, a new multi-chip module with Google Edge TPU

Coral Accelerator Module, a new multi-chip module with Google Edge TPU

Next, we’re announcing the Coral Dev Board Mini, which provides a smaller form-factor, lower-power, and lower-cost alternative to the Coral Dev Board. The Mini combines the new Coral Accelerator Module with the MediaTek 8167s SoC to create a board that excels at 720P video encoding/decoding and computer vision use cases. The board will be on display during CES 2020 at the MediaTek showcase located in the Venetian, Tech West, Level 3. The Coral Dev Board Mini will be available in the first half of 2020.

We're also offering new variations to the Coral System-on-Module, now available with 2GB and 4GB LPDDR4 RAM in addition to the original 1GB LPDDR4 configuration. We’ll be showcasing how the SoM can be used in smart city, manufacturing, and healthcare applications, as well as a few new SoC and MCU explorations we’ve been working on with the NXP team at CES 2020 in their pavilion located at the Las Vegas Convention Center, Tech East, Central Plaza, CP-18.

Finally, Asus has chosen the Coral SOM as the base to their Tinker Edge T product, a maker friendly single-board computer that features a rich set of I/O interfaces, multiple camera connectors, programmable LEDs, and color-coded GPIO header. The Tinker Edge T board will be available soon -- more details can be found here from Asus.

Come visit Coral at CES Jan 7-10 in Las Vegas:

  • NXP exhibit (LVCC, Tech East, Central Plaza, CP-18)
  • Mediatek exhibit (Venetian, Tech West, Level 3)
  • Murata exhibit (LVCC, South Hall 2, MP26061)

And, as always, we are always looking for ways to improve the platform, so keep reaching out to us at [email protected]

Updates from Coral: Mendel Linux 4.0 and much more!

Posted by Carlos Mendonça (Product Manager), Coral TeamIllustration of the Coral Dev Board placed next to Fall foliage

Last month, we announced that Coral graduated out of beta, into a wider, global release. Today, we're announcing the next version of Mendel Linux (4.0 release Day) for the Coral Dev Board and SoM, as well as a number of other exciting updates.

We have made significant updates to improve performance and stability. Mendel Linux 4.0 release Day is based on Debian 10 Buster and includes upgraded GStreamer pipelines and support for Python 3.7, OpenCV, and OpenCL. The Linux kernel has also been updated to version 4.14 and U-Boot to version 2017.03.3.

We’ve also made it possible to use the Dev Board's GPU to convert YUV to RGB pixel data at up to 130 frames per second on 1080p resolution, which is one to two orders of magnitude faster than on Mendel Linux 3.0 release Chef. These changes make it possible to run inferences with YUV-producing sources such as cameras and hardware video decoders.

To upgrade your Dev Board or SoM, follow our guide to flash a new system image.

MediaPipe on Coral

MediaPipe is an open-source, cross-platform framework for building multi-modal machine learning perception pipelines that can process streaming data like video and audio. For example, you can use MediaPipe to run on-device machine learning models and process video from a camera to detect, track and visualize hand landmarks in real-time.

Developers and researchers can prototype their real-time perception use cases starting with the creation of the MediaPipe graph on desktop. Then they can quickly convert and deploy that same graph to the Coral Dev Board, where the quantized TensorFlow Lite model will be accelerated by the Edge TPU.

As part of this first release, MediaPipe is making available new experimental samples for both object and face detection, with support for the Coral Dev Board and SoM. The source code and instructions for compiling and running each sample are available on GitHub and on the MediaPipe documentation site.

New Teachable Sorter project tutorial

New Teachable Sorter project tutorial

A new Teachable Sorter tutorial is now available. The Teachable Sorter is a physical sorting machine that combines the Coral USB Accelerator's ability to perform very low latency inference with an ML model that can be trained to rapidly recognize and sort different objects as they fall through the air. It leverages Google’s new Teachable Machine 2.0, a web application that makes it easy for anyone to quickly train a model in a fun, hands-on way.

The tutorial walks through how to build the free-fall sorter, which separates marshmallows from cereal and can be trained using Teachable Machine.

Coral is now on TensorFlow Hub

Earlier this month, the TensorFlow team announced a new version of TensorFlow Hub, a central repository of pre-trained models. With this update, the interface has been improved with a fresh landing page and search experience. Pre-trained Coral models compiled for the Edge TPU continue to be available on our Coral site, but a select few are also now available from the TensorFlow Hub. On the site, you can find models featuring an Overlay interface, allowing you to test the model's performance against a custom set of images right from the browser. Check out the experience for MobileNet v1 and MobileNet v2.

We are excited to share all that Coral has to offer as we continue to evolve our platform. For a list of worldwide distributors, system integrators and partners, visit the new Coral partnerships page. We hope you’ll use the new features offered on Coral.ai as a resource and encourage you to keep sending us feedback at [email protected].

Coral moves out of beta

Posted by Vikram Tank (Product Manager), Coral Team

microchips on coral colored background

Last March, we launched Coral beta from Google Research. Coral helps engineers and researchers bring new models out of the data center and onto devices, running TensorFlow models efficiently at the edge. Coral is also at the core of new applications of local AI in industries ranging from agriculture to healthcare to manufacturing. We've received a lot of feedback over the past six months and used it to improve our platform. Today we’re thrilled to graduate Coral out of beta, into a wider, global release.

Coral is already delivering impact across industries, and several of our partners are including Coral in products that require fast ML inferencing at the edge.

In healthcare, Care.ai is using Coral to build a device that enables hospitals and care centers to respond quickly to falls, prevent bed sores, improve patient care, and reduce costs. Virgo SVS is also using Coral as the basis of a polyp detection system that helps doctors improve the accuracy of endoscopies.

In a very different use case, Olea Edge employs Coral to help municipal water utilities accurately measure the amount of water used by their commercial customers. Their Meter Health Analytics solution uses local AI to reduce waste and predict equipment failure in industrial water meters.

Nexcom is using Coral to build gateways with local AI and provide a platform for next-gen, AI-enabled IoT applications. By moving AI processing to the gateway, existing sensor networks can stay in service without the need to add AI processing to each node.

From prototype to production

Microchips on white background

Coral’s Dev Board is designed as an integrated prototyping solution for new product development. Under the heatsink is the detachable Coral SoM, which combines Google’s Edge TPU with the NXP IMX8M SoC, Wi-Fi and Bluetooth connectivity, memory, and storage. We’re happy to announce that you can now purchase the Coral SoM standalone. We’ve also created a baseboard developer guide to help integrate it into your own production design.

Our Coral USB Accelerator allows users with existing system designs to add local AI inferencing via USB 2/3. For production workloads, we now offer three new Accelerators that feature the Edge TPU and connect via PCIe interfaces: Mini PCIe, M.2 A+E key, and M.2 B+M key. You can easily integrate these Accelerators into new products or upgrade existing devices that have an available PCIe slot.

The new Coral products are available globally and for sale at Mouser; for large volume sales, contact our sales team. By the end of 2019, we'll continue to expand our distribution of the Coral Dev Board and SoM into new markets including: Taiwan, Australia, New Zealand, India, Thailand, Singapore, Oman, Ghana and the Philippines.

Better resources

We’ve also revamped the Coral site with better organization for our docs and tools, a set of success stories, and industry focused pages. All of it can be found at a new, easier to remember URL Coral.ai.

To help you get the most out of the hardware, we’re also publishing a new set of examples. The included models and code can provide solutions to the most common on-device ML problems, such as image classification, object detection, pose estimation, and keyword spotting.

For those looking for a more in-depth application—and a way to solve the eternal problem of squirrels plundering your bird feeder—the Smart Bird Feeder project shows you how to perform classification with a custom dataset on the Coral Dev board.

Finally, we’ll soon release a new version of the Mendel OS that updates the system to Debian Buster, and we're hard at work on more improvements to the Edge TPU compiler and runtime that will improve the model development workflow.

The official launch of Coral is, of course, just the beginning, and we’ll continue to evolve the platform. Please keep sending us feedback at [email protected].

Coral summer updates: Post-training quant support, TF Lite delegate, and new models!

Posted by Vikram Tank (Product Manager), Coral Team

Summer updates cartoon

Coral’s had a busy summer working with customers, expanding distribution, and building new features — and of course taking some time for R&R. We’re excited to share updates, early work, and new models for our platform for local AI with you.

The compiler has been updated to version 2.0, adding support for models built using post-training quantization—only when using full integer quantization (previously, we required quantization-aware training)—and fixing a few bugs. As the Tensorflow team mentions in their Medium post “post-training integer quantization enables users to take an already-trained floating-point model and fully quantize it to only use 8-bit signed integers (i.e. `int8`).” In addition to reducing the model size, models that are quantized with this method can now be accelerated by the Edge TPU found in Coral products.

We've also updated the Edge TPU Python library to version 2.11.1 to include new APIs for transfer learning on Coral products. The new on-device back propagation API allows you to perform transfer learning on the last layer of an image classification model. The last layer of a model is removed before compilation and implemented on-device to run on the CPU. It allows for near-real time transfer learning and doesn’t require you to recompile the model. Our previously released imprinting API, has been updated to allow you to quickly retrain existing classes or add new ones while leaving other classes alone. You can now even keep the classes from the pre-trained base model. Learn more about both options for on-device transfer learning.

Until now, accelerating your model with the Edge TPU required that you write code using either our Edge TPU Python API or in C++. But now you can accelerate your model on the Edge TPU when using the TensorFlow Lite interpreter API, because we've released a TensorFlow Lite delegate for the Edge TPU. The TensorFlow Lite Delegate API is an experimental feature in TensorFlow Lite that allows for the TensorFlow Lite interpreter to delegate part or all of graph execution to another executor—in this case, the other executor is the Edge TPU. Learn more about the TensorFlow Lite delegate for Edge TPU.

Coral has also been working with Edge TPU and AutoML teams to release EfficientNet-EdgeTPU: a family of image classification models customized to run efficiently on the Edge TPU. The models are based upon the EfficientNet architecture to achieve the image classification accuracy of a server-side model in a compact size that's optimized for low latency on the Edge TPU. You can read more about the models’ development and performance on the Google AI Blog, and download trained and compiled versions on the Coral Models page.

And, as summer comes to an end we also want to share that Arrow offers a student teacher discount for those looking to experiment with the boards in class or the lab this year.

We're excited to keep evolving the Coral platform, please keep sending us feedback at [email protected].

Coral updates: Project tutorials, a downloadable compiler, and a new distributor

Posted by Vikram Tank (Product Manager), Coral Team

coral hardware

We’re committed to evolving Coral to make it even easier to build systems with on-device AI. Our team is constantly working on new product features, and content that helps ML practitioners, engineers, and prototypers create the next generation of hardware.

To improve our toolchain, we're making the Edge TPU Compiler available to users as a downloadable binary. The binary works on Debian-based Linux systems, allowing for better integration into custom workflows. Instructions on downloading and using the binary are on the Coral site.

We’re also adding a new section to the Coral site that showcases example projects you can build with your Coral board. For instance, Teachable Machine is a project that guides you through building a machine that can quickly learn to recognize new objects by re-training a vision classification model directly on your device. Minigo shows you how to create an implementation of AlphaGo Zero and run it on the Coral Dev Board or USB Accelerator.

Our distributor network is growing as well: Arrow will soon sell Coral products.

Updates from Coral: A new compiler and much more

Posted by Vikram Tank (Product Manager), Coral Team

Coral has been public for about a month now, and we’ve heard some great feedback about our products. As we evolve the Coral platform, we’re making our products easier to use and exposing more powerful tools for building devices with on-device AI.

Today, we're updating the Edge TPU model compiler to remove the restrictions around specific architectures, allowing you to submit any model architecture that you want. This greatly increases the variety of models that you can run on the Coral platform. Just be sure to review the TensorFlow ops supported on Edge TPU and model design requirements to take full advantage of the Edge TPU at runtime.

We're also releasing a new version of Mendel OS (3.0 Chef) for the Dev Board with a new board management tool called Mendel Development Tool (MDT).

To help with the developer workflow, our new C++ API works with the TensorFlow Lite C++ API so you can execute inferences on an Edge TPU. In addition, both the Python and C++ APIs now allow you to run multiple models in parallel, using multiple Edge TPU devices.

In addition to these updates, we’re adding new capabilities to Coral with the release of the Environmental Sensor Board. It’s an accessory board for the Coral Dev Platform (and Raspberry Pi) that brings sensor input to your models. It has integrated light, temperature, humidity, and barometric sensors, and the ability to add more sensors via it's four Grove connectors. The secure element on-board also allows for easy communication with the Google Cloud IOT Core.

The team has also been working with partners to help them evaluate whether Coral is the right fit for their products. We’re excited that Oivi has chosen us to be the base platform of their new handheld AI-camera. This product will help prevent blindness among diabetes patients by providing early, automated detection of diabetic retinopathy. Anders Eikenes, CEO of Oivi, says “Oivi is dedicated towards providing patient-centric eye care for everyone - including emerging markets. We were honoured to be selected by Google to participate in their Coral alpha program, and are looking forward to our continued cooperation. The Coral platform gives us the ability to run our screening ML models inside a handheld device; greatly expanding the access and ease of diabetic retinopathy screening.”

Finally, we’re expanding our distributor network to make it easier to get Coral boards into your hands around the world. This month, Seeed and NXP will begin to sell Coral products, in addition to Mouser.

We're excited to keep evolving the Coral platform, please keep sending us feedback at [email protected].

You can see the full release notes on Coral site.

Introducing Coral: Our platform for development with local AI

Posted by Billy Rutledge (Director) and Vikram Tank (Product Mgr), Coral Team

AI can be beneficial for everyone, especially when we all explore, learn, and build together. To that end, Google's been developing tools like TensorFlow and AutoML to ensure that everyone has access to build with AI. Today, we're expanding the ways that people can build out their ideas and products by introducing Coral into public beta.

Coral is a platform for building intelligent devices with local AI.

Coral offers a complete local AI toolkit that makes it easy to grow your ideas from prototype to production. It includes hardware components, software tools, and content that help you create, train and run neural networks (NNs) locally, on your device. Because we focus on accelerating NN's locally, our products offer speedy neural network performance and increased privacy — all in power-efficient packages. To help you bring your ideas to market, Coral components are designed for fast prototyping and easy scaling to production lines.

Our first hardware components feature the new Edge TPU, a small ASIC designed by Google that provides high-performance ML inferencing for low-power devices. For example, it can execute state-of-the-art mobile vision models such as MobileNet V2 at 100+ fps, in a power efficient manner.

Coral Camera Module, Dev Board and USB Accelerator

For new product development, the Coral Dev Board is a fully integrated system designed as a system on module (SoM) attached to a carrier board. The SoM brings the powerful NXP iMX8M SoC together with our Edge TPU coprocessor (as well as Wi-Fi, Bluetooth, RAM, and eMMC memory). To make prototyping computer vision applications easier, we also offer a Camera that connects to the Dev Board over a MIPI interface.

To add the Edge TPU to an existing design, the Coral USB Accelerator allows for easy integration into any Linux system (including Raspberry Pi boards) over USB 2.0 and 3.0. PCIe versions are coming soon, and will snap into M.2 or mini-PCIe expansion slots.

When you're ready to scale to production we offer the SOM from the Dev Board and PCIe versions of the Accelerator for volume purchase. To further support your integrations, we'll be releasing the baseboard schematics for those who want to build custom carrier boards.

Our software tools are based around TensorFlow and TensorFlow Lite. TF Lite models must be quantized and then compiled with our toolchain to run directly on the Edge TPU. To help get you started, we're sharing over a dozen pre-trained, pre-compiled models that work with Coral boards out of the box, as well as software tools to let you re-train them.

For those building connected devices with Coral, our products can be used with Google Cloud IoT. Google Cloud IoT combines cloud services with an on-device software stack to allow for managed edge computing with machine learning capabilities.

Coral products are available today, along with product documentation, datasheets and sample code at g.co/coral. We hope you try our products during this public beta, and look forward to sharing more with you at our official launch.

New AIY Edge TPU Boards

Posted by Billy Rutledge, Director of AIY Projects

Over the past year and a half, we've seen more than 200K people build, modify, and create with our Voice Kit and Vision Kit products. Today at Cloud Next we announced two new devices to help professional engineers build new products with on-device machine learning(ML) at their core: the AIY Edge TPU Dev Board and the AIY Edge TPU Accelerator. Both are powered by Google's Edge TPU and represent our first steps towards expanding AIY into a platform for experimentation with on-device ML.

The Edge TPU is Google's purpose-built ASIC chip designed to run TensorFlow Lite ML models on your device. We've learned that performance-per-watt and performance-per-dollar are critical benchmarks when processing neural networks within a small footprint. The Edge TPU delivers both in a package that's smaller than the head of a penny. It can accelerate ML inferencing on device, or can pair with Google Cloud to create a full cloud-to-edge ML stack. In either configuration, by processing data directly on-device, a local ML accelerator increases privacy, removes the need for persistent connections, reduces latency, and allows for high performance using less power.

The AIY Edge TPU Dev Board is an all-in-one development board that allows you to prototype embedded systems that demand fast ML inferencing. The baseboard provides all the peripheral connections you need to effectively prototype your device — including a 40-pin GPIO header to integrate with various electrical components. The board also features a removable System-on-module (SOM) daughter board can be directly integrated into your own hardware once you're ready to scale.

The AIY Edge TPU Accelerator is a neural network coprocessor for your existing system. This small USB-C stick can connect to any Linux-based system to perform accelerated ML inferencing. The casing includes mounting holes for attachment to host boards such as a Raspberry Pi Zero or your custom device.

On-device ML is still in its early days, and we're excited to see how these two products can be applied to solve real world problems — such as increasing manufacturing equipment reliability, detecting quality control issues in products, tracking retail foot-traffic, building adaptive automotive sensing systems, and more applications that haven't been imagined yet.

Both devices will be available online this fall in the US with other countries to follow shortly.

For more product information visit g.co/aiy and sign up to be notified as products become available.

Android Things Release Candidate

Posted by Dave Smith, Developer Advocate for IoT

Earlier this year at CES, we showcased consumer products powered by Android Things from partners like Lenovo, LG, JBL, iHome, and Sony. We are excited to see Android Things enable the wider developer ecosystem as well. Today we are announcing the final preview release of Android Things, Developer Preview 8, before the upcoming stable release.

Feature complete SDK

Developer Preview 8 represents the final API surface exposed in the Android Things support library for the upcoming stable release. There will be no more breaking API changes before the stable v1.0 release of the SDK. For details on all the API changes included in DP8, see the release notes. Refer to the updated SDK reference to review the classes and methods in the final SDK.

This release also brings new features in the Android Things developer console to make building and managing production devices easier. Here are some notable updates:

Production-focused console enhancements

With an eye towards building and shipping production devices with the upcoming LTS release, we have made several updates to the Android Things developer console:

  • Enhanced OTA: Unpublish the current OTA build when issues are discovered in the field.
  • Visual storage layout: Configure the device storage allocated to apps and data for each build, and get an overview of how much storage your apps require.
  • Font/locale controls: Configure the set of supported fonts and locales packaged into each build.
  • Group sharing: Product sharing has been extended to include support for Google Groups.

App library

The new app library enables you to manage APKs more easily without the need to package them together in a separate zipped bundle. Track individual versions, review permissions, and share your apps with other console users. See the app library documentation for more details.

Permissions

On mobile devices, apps request permissions at runtime and the end user grants them. In earlier previews, Android Things granted these same permissions automatically to apps on device boot. Beginning in DP8, these permissions are granted using a new interface in the developer console, giving developers more control of the permissions used by the apps on their device.

This change does not affect development, as Android Studio grants all permissions by default. Developers using the command line can append the -g flag to the adb install command to get the same behavior. To test how apps on your device behave with certain permissions revoked, use the pm command:

$ adb shell pm [grant|revoke] <permission-name> ...

App launch behavior

Embedded devices need to launch their primary application automatically after the device boots, and relaunch it if the app terminates unexpectedly. In earlier previews, the main app on the device could listen for a custom IOT_LAUNCHER intent to enable this behavior. Beginning in DP8, this category is replaced by the standard CATEGORY_HOME intent.

<activity android:name=".HomeActivity">
    ...

    <!-- Launch activity automatically on boot, relaunch on termination. -->
    <intent-filter>
        <action android:name="android.intent.action.MAIN"/>
        <category android:name="android.intent.category.HOME"/>
        <category android:name="android.intent.category.DEFAULT"/>
    </intent-filter>
</activity>

Apps that contain an IOT_LAUNCHER intent filter will no longer be triggered on boot. Update your apps to use CATEGORY_HOME instead.

Feedback

Thanks to all of you in the developer community for sharing your feedback with us throughout developer preview. Join Google's IoT Developers Community on Google+ to let us know what you're building with Android Things and how we can improve the platform in future releases to help you build connected devices at scale!

AIY Projects: Updated kits for 2018

Posted by Billy Rutledge, Director of AIY Projects

Last year, AIY Projects launched to give makers the power to build AI into their projects with two do-it-yourself kits. We're seeing continued demand for the kits, especially from the STEM audience where parents and teachers alike have found the products to be great tools for the classroom. The changing nature of work in the future means students may have jobs that haven't yet been imagined, and we know that computer science skills, like analytical thinking and creative problem solving, will be crucial.

We're taking the first of many steps to help educators integrate AIY into STEM lesson plans and help prepare students for the challenges of the future by launching a new version of our AIY kits. The Voice Kit lets you build a voice controlled speaker, while the Vision Kit lets you build a camera that learns to recognize people and objects (check it out here). The new kits make getting started a little easier with clearer instructions, a new app and all the parts in one box.

To make setup easier, both kits have been redesigned to work with the new Raspberry Pi Zero WH, which comes included in the box, along with the USB connector cable and pre-provisioned SD card. Now users no longer need to download the software image and can get running faster. The updated AIY Vision Kit v1.1 also includes the Raspberry Pi Camera v2.

AIY Voice Kit v2 includes Raspberry Pi Zero WH and pre-provisioned SD card

AIY Voice Kit v1.1 includes Raspberry Pi Zero WH, Raspberry Pi Cam 2 and pre-provisioned SD card

We're also introducing the AIY companion app for Android, available here in Google Play, to make wireless setup and configuration a snap. The kits still work with monitor, keyboard and mouse as an alternate path and we're working on iOS and Chrome companions which will be coming soon.

The AIY website has been refreshed with improved documentation, now easier for young makers to get started and learn as they build. It also includes a new AIY Models area, showcasing a collection of neural networks designed to work with AIY kits. While we've solved one barrier to entry for the STEM audience, we recognize that there are many other things that we can do to make our kits even more useful. We'll once again be at #MakerFaire events to gather feedback from our users and in June we'll be working with teachers from all over the world at the ISTE conference in Chicago.

The new AIY Voice Kit and Vision Kit have arrived at Target Stores and Target.com (US) this month and we're working to make them globally available through retailers worldwide. Sign up on our mailing list to be notified when our products become available.

We hope you'll pick up one of the new AIY kits and learn more about how to build your own smart devices. Be sure to share your recipes on Hackster.io and social media using #aiyprojects.