Tag Archives: Coral

Coral makes edge AI even more accessible in 2020

Posted by the Coral team

Coral Dev Board Mini and Accelerator Module feature Google's Edge TPU co-processor to accelerate AI at the edge.

Since we launched Coral back in March 2019, we’ve added a number of new product form factors to accommodate the many ways users are adding on-device ML to their products. We've also streamlined the ML workflow and added capabilities like model pipelining with multiple Edge TPUs for an easier and more robust developer experience. And from this, we’ve helped enable amazing use cases from smart water meters that prevent water loss with Olea Edge, to systems for improving harvest yield with Farmwave, to noise cancellation in meetings in Google’s own Series One meeting kits.

This week, we’ll begin shipping the Coral Accelerator Module, a multi-chip module that combines the Edge TPU and it’s power circuitry into a solderable package. The module exposes PCIe and USB2 interfaces, which make it even easier to integrate Coral into custom designs. Several companies are already taking advantage of the compact size and capabilities with their new products coming to market. Read more about how Gumstix, STD, Siana Systems and IEI are using our module.

And in December, we’ll begin shipping the Dev Board Mini, a smaller, more power-efficient, and value-oriented board that brings forward a more traditional, flattened single-board computer design. The Dev Board Mini pairs a Mediatek 8167 SoC with the Coral Accelerator Module over USB 2 and is a great way to evaluate the module as the center of a project or deployment.

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

We recently announced a new version of the Coral ML APIs and tools. This release brings the C++ API into parity with Python and makes it more modular, reusable and performant. At the same time it eliminates unnecessary abstractions and surfaces replacing them with native TensorFlow Lite APIs. This release also graduates the Model Pipelining API out of beta and introduces a new model partitioner that automatically partitions models based on profiling and up to 10x better performance.

We’ve added a pre-trained version of MobileDet — a state-of-the-art object detection model for mobile systems — into our models portfolio. We’re migrating our model-development workflow to TensorFlow 2, and we’re including a handful of updated or new models based on the TF2 Keras framework. For details, check out the full announcement on the TensorFlow blog.

We’re also excited to see great developer tools coming from our ecosystem partners. For example, PerceptiLabs offers a visual API for building TensorFlow models and recently published a new demo which trains a machine learning model to identify sign language optimized for the edge with Coral.

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

And SigFox released a radio transceiver board that stacks on either the Coral Dev Board or Dev Board Mini. This allows small data payloads to be transmitted across low power, long range radio networks for use cases like smart cities, fleet management, asset tracking, agriculture and energy. The PCB design will be offered as a free download on SigFox’s website. Google Cloud Solutions Architect Markku Lepisto will present the new design today, in the opening keynote at SigFox Connect.

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Just in time for harvest we wanted to share a story about how Farmwave is using Coral to improve the efficiency of farm equipment and reduce food waste. Traditional yield loss analysis involves hand-counting grains of corn left on the ground mid harvest. It’s a time and labor intensive task, and not feasible for farmers who measure the value of their half-million-dollar combines in minutes spent running them.

By leveraging Coral’s on-device AI capabilities, Farmwave was able to build a system that automates the count while the machine is running. Thus allowing farmers to make real-time adjustments to harvesting machines in response to conditions in the field, which can make a big difference in yield.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Revolving Sushi Bar in Japan has always been committed to the highest standards of health and safety for its customers. Known for their tech forward approach, Kura has dabbled in sushi making robots, an automated prize machine called Bikkura-pon, and a patented dome-shaped dish cover, aptly dubbed Mr. Fresh. But most recently, Kura has used Coral to develop an AI powered system that not only facilitates efficiency for better customer experiences, but also enables better tracking to prevent foodborne illnesses.

Making AI more accessible

While this year has presented the world with many obstacles, we’ve been impressed by the new ideas and innovations coming forward through technology. By providing the necessary tools and technology for edge AI, we strive to empower society to create affordable, adaptable, and intelligent systems.

We are excited to share all that Coral has to offer as we evolve our platform. 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 and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

Coral makes edge AI even more accessible in 2020

Posted by the Coral team

Coral Dev Board Mini and Accelerator Module feature Google's Edge TPU co-processor to accelerate AI at the edge.

Since we launched Coral back in March 2019, we’ve added a number of new product form factors to accommodate the many ways users are adding on-device ML to their products. We've also streamlined the ML workflow and added capabilities like model pipelining with multiple Edge TPUs for an easier and more robust developer experience. And from this, we’ve helped enable amazing use cases from smart water meters that prevent water loss with Olea Edge, to systems for improving harvest yield with Farmwave, to noise cancellation in meetings in Google’s own Series One meeting kits.

This week, we’ll begin shipping the Coral Accelerator Module, a multi-chip module that combines the Edge TPU and it’s power circuitry into a solderable package. The module exposes PCIe and USB2 interfaces, which make it even easier to integrate Coral into custom designs. Several companies are already taking advantage of the compact size and capabilities with their new products coming to market. Read more about how Gumstix, STD, Siana Systems and IEI are using our module.

And in December, we’ll begin shipping the Dev Board Mini, a smaller, more power-efficient, and value-oriented board that brings forward a more traditional, flattened single-board computer design. The Dev Board Mini pairs a Mediatek 8167 SoC with the Coral Accelerator Module over USB 2 and is a great way to evaluate the module as the center of a project or deployment.

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

We recently announced a new version of the Coral ML APIs and tools. This release brings the C++ API into parity with Python and makes it more modular, reusable and performant. At the same time it eliminates unnecessary abstractions and surfaces replacing them with native TensorFlow Lite APIs. This release also graduates the Model Pipelining API out of beta and introduces a new model partitioner that automatically partitions models based on profiling and up to 10x better performance.

We’ve added a pre-trained version of MobileDet — a state-of-the-art object detection model for mobile systems — into our models portfolio. We’re migrating our model-development workflow to TensorFlow 2, and we’re including a handful of updated or new models based on the TF2 Keras framework. For details, check out the full announcement on the TensorFlow blog.

We’re also excited to see great developer tools coming from our ecosystem partners. For example, PerceptiLabs offers a visual API for building TensorFlow models and recently published a new demo which trains a machine learning model to identify sign language optimized for the edge with Coral.

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

And SigFox released a radio transceiver board that stacks on either the Coral Dev Board or Dev Board Mini. This allows small data payloads to be transmitted across low power, long range radio networks for use cases like smart cities, fleet management, asset tracking, agriculture and energy. The PCB design will be offered as a free download on SigFox’s website. Google Cloud Solutions Architect Markku Lepisto will present the new design today, in the opening keynote at SigFox Connect.

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Just in time for harvest we wanted to share a story about how Farmwave is using Coral to improve the efficiency of farm equipment and reduce food waste. Traditional yield loss analysis involves hand-counting grains of corn left on the ground mid harvest. It’s a time and labor intensive task, and not feasible for farmers who measure the value of their half-million-dollar combines in minutes spent running them.

By leveraging Coral’s on-device AI capabilities, Farmwave was able to build a system that automates the count while the machine is running. Thus allowing farmers to make real-time adjustments to harvesting machines in response to conditions in the field, which can make a big difference in yield.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Revolving Sushi Bar in Japan has always been committed to the highest standards of health and safety for its customers. Known for their tech forward approach, Kura has dabbled in sushi making robots, an automated prize machine called Bikkura-pon, and a patented dome-shaped dish cover, aptly dubbed Mr. Fresh. But most recently, Kura has used Coral to develop an AI powered system that not only facilitates efficiency for better customer experiences, but also enables better tracking to prevent foodborne illnesses.

Making AI more accessible

While this year has presented the world with many obstacles, we’ve been impressed by the new ideas and innovations coming forward through technology. By providing the necessary tools and technology for edge AI, we strive to empower society to create affordable, adaptable, and intelligent systems.

We are excited to share all that Coral has to offer as we evolve our platform. 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 and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

Coral makes edge AI even more accessible in 2020

Posted by the Coral team

Coral Dev Board Mini and Accelerator Module feature Google's Edge TPU co-processor to accelerate AI at the edge.

Since we launched Coral back in March 2019, we’ve added a number of new product form factors to accommodate the many ways users are adding on-device ML to their products. We've also streamlined the ML workflow and added capabilities like model pipelining with multiple Edge TPUs for an easier and more robust developer experience. And from this, we’ve helped enable amazing use cases from smart water meters that prevent water loss with Olea Edge, to systems for improving harvest yield with Farmwave, to noise cancellation in meetings in Google’s own Series One meeting kits.

This week, we’ll begin shipping the Coral Accelerator Module, a multi-chip module that combines the Edge TPU and it’s power circuitry into a solderable package. The module exposes PCIe and USB2 interfaces, which make it even easier to integrate Coral into custom designs. Several companies are already taking advantage of the compact size and capabilities with their new products coming to market. Read more about how Gumstix, STD, Siana Systems and IEI are using our module.

And in December, we’ll begin shipping the Dev Board Mini, a smaller, more power-efficient, and value-oriented board that brings forward a more traditional, flattened single-board computer design. The Dev Board Mini pairs a Mediatek 8167 SoC with the Coral Accelerator Module over USB 2 and is a great way to evaluate the module as the center of a project or deployment.

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

We recently announced a new version of the Coral ML APIs and tools. This release brings the C++ API into parity with Python and makes it more modular, reusable and performant. At the same time it eliminates unnecessary abstractions and surfaces replacing them with native TensorFlow Lite APIs. This release also graduates the Model Pipelining API out of beta and introduces a new model partitioner that automatically partitions models based on profiling and up to 10x better performance.

We’ve added a pre-trained version of MobileDet — a state-of-the-art object detection model for mobile systems — into our models portfolio. We’re migrating our model-development workflow to TensorFlow 2, and we’re including a handful of updated or new models based on the TF2 Keras framework. For details, check out the full announcement on the TensorFlow blog.

We’re also excited to see great developer tools coming from our ecosystem partners. For example, PerceptiLabs offers a visual API for building TensorFlow models and recently published a new demo which trains a machine learning model to identify sign language optimized for the edge with Coral.

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

And SigFox released a radio transceiver board that stacks on either the Coral Dev Board or Dev Board Mini. This allows small data payloads to be transmitted across low power, long range radio networks for use cases like smart cities, fleet management, asset tracking, agriculture and energy. The PCB design will be offered as a free download on SigFox’s website. Google Cloud Solutions Architect Markku Lepisto will present the new design today, in the opening keynote at SigFox Connect.

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Just in time for harvest we wanted to share a story about how Farmwave is using Coral to improve the efficiency of farm equipment and reduce food waste. Traditional yield loss analysis involves hand-counting grains of corn left on the ground mid harvest. It’s a time and labor intensive task, and not feasible for farmers who measure the value of their half-million-dollar combines in minutes spent running them.

By leveraging Coral’s on-device AI capabilities, Farmwave was able to build a system that automates the count while the machine is running. Thus allowing farmers to make real-time adjustments to harvesting machines in response to conditions in the field, which can make a big difference in yield.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Revolving Sushi Bar in Japan has always been committed to the highest standards of health and safety for its customers. Known for their tech forward approach, Kura has dabbled in sushi making robots, an automated prize machine called Bikkura-pon, and a patented dome-shaped dish cover, aptly dubbed Mr. Fresh. But most recently, Kura has used Coral to develop an AI powered system that not only facilitates efficiency for better customer experiences, but also enables better tracking to prevent foodborne illnesses.

Making AI more accessible

While this year has presented the world with many obstacles, we’ve been impressed by the new ideas and innovations coming forward through technology. By providing the necessary tools and technology for edge AI, we strive to empower society to create affordable, adaptable, and intelligent systems.

We are excited to share all that Coral has to offer as we evolve our platform. 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 and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

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.

Summer updates from Coral

Posted by the Coral Team

Summer has arrived along with a number of Coral updates. We're happy to announce a new partnership with balena that helps customers build, manage, and deploy IoT applications at scale on Coral devices. In addition, we've released a series of updates to expand platform compatibility, make development easier, and improve the ML capabilities of our devices.

Open-source Edge TPU runtime now available on GitHub

First up, our Edge TPU runtime is now open-source and available on GitHub, including scripts and instructions for building the library for Linux and Windows. Customers running a platform that is not officially supported by Coral, including ARMv7 and RISC-V can now compile the Edge TPU runtime themselves and start experimenting. An open source runtime is easier to integrate into your customized build pipeline, enabling support for creating Yocto-based images as well as other distributions.

Windows drivers now available for the Mini PCIe and M.2 accelerators

Coral customers can now also use the Mini PCIe and M.2 accelerators on the Microsoft Windows platform. New Windows drivers for these products complement the previously released Windows drivers for the USB accelerator and make it possible to start prototyping with the Coral USB Accelerator on Windows and then to move into production with our Mini PCIe and M.2 products.

New fresh bits on the Coral ML software stack

We’ve also made a number of new updates to our ML tools:

  • The Edge TPU compiler is now version 14.1. It can be updated by running sudo apt-get update && sudo apt-get install edgetpu, or follow the instructions here
  • Our new Model Pipelining API allows you to divide your model across multiple Edge TPUs. The C++ version is currently in beta and the source is on GitHub
  • New embedding extractor models for EfficientNet, for use with on-device backpropagation. Embedding extractor models are compiled with the last fully-connected layer removed, allowing you to retrain for classification. Previously, only Inception and MobileNet were available and now retraining can also be done on EfficientNet
  • New Colab notebooks to retrain a classification model with TensorFlow 2.0 and build C++ examples

Balena partners with Coral to enable AI at the edge

We are excited to share that the Balena fleet management platform now supports Coral products!

Companies running a fleet of ML-enabled devices on the edge need to keep their systems up-to-date with the latest security patches in order to protect data, model IP and hardware from being compromised. Additionally, ML applications benefit from being consistently retrained to recognize new use cases with maximum accuracy. Coral + balena together, bring simplicity and ease to the provisioning, deployment, updating, and monitoring of your ML project at the edge, moving early prototyping seamlessly towards production environments with many thousands of devices.

Read more about all the benefits of Coral devices combined with balena container technology or get started deploying container images to your Coral fleet with this demo project.

New version of Mendel Linux

Mendel Linux (5.0 release Eagle) is now available for the Coral Dev Board and SoM and includes a more stable package repository that provides a smoother updating experience. It also brings compatibility improvements and a new version of the GPU driver.

New models

Last but not least, we’ve recently released BodyPix, a Google person-segmentation model that was previously only available for TensorFlow.JS, as a Coral model. This enables real-time privacy preserving understanding of where people (and body parts) are on a camera frame. We first demoed this at CES 2020 and it was one of our most popular demos. Using BodyPix we can remove people from the frame, display only their outline, and aggregate over time to see heat maps of population flow.

Here are two possible applications of BodyPix: Body-part segmentation and anonymous population flow. Both are running on the Dev Board.

We’re excited to add BodyPix to the portfolio of projects the community is using to extend our models far beyond our demos—including tackling today’s biggest challenges. For example, Neuralet has taken our MobileNet V2 SSD Detection model and used it to implement Smart Social Distancing. Using the bounding box of person detection, they can compute a region for safe distancing and let a user know if social distance isn’t being maintained. The best part is this is done without any sort of facial recognition or tracking, with Coral we can accomplish this in real-time in a privacy preserving manner.

We can’t wait to see more projects that the community can make with BodyPix. Beyond anonymous population flow there’s endless possibilities with background and body part manipulation. Let us know what you come up with at our community channels, including GitHub and StackOverflow.

________________________

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, including balena, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform and share your feedback at [email protected].

Building a more resilient world together

Posted by Billy Rutledge, Director of the Coral team

UNDP Hackster.io COVID19 Detect Protect Poster

Recently, we’ve seen communities respond to the challenges of the coronavirus pandemic by using technology in new ways to effect positive change. It’s increasingly important that our systems are able to adapt to new contexts, handle disruptions, and remain efficient.

At Coral, we believe intelligence at the edge is a key ingredient towards building a more resilient future. By making the latest machine learning tools easy-to-use and accessible, innovators can collaborate to create solutions that are most needed in their communities. Developers are already using Coral to build solutions that can understand and react in real-time, while maintaining privacy for everyone present.

Helping our communities stay safe, together

As mandatory isolation measures begin to relax, compliance with safe social distancing protocol has become a topic of primary concern for experts across the globe. Businesses and individuals have been stepping up to find ways to use technology to help reduce the risk and spread. Many efforts are employing the benefits of edge AI—here are a few early stage examples that have inspired us.

woman and child crossing the street

In Belgium, engineers at Edgise recently used Coral to develop an occupancy monitor to aid businesses in managing capacity. With the privacy preserving properties of edge AI, businesses can anonymously count how many customers enter and exit a space, signaling when the area is too full.

A research group at the Sathyabama Institute of Science and Technology in India are using Coral to develop a wearable device to serve as a COVID-19 cough counter and health monitor, allowing medical professionals to better care for low risk patients in an outpatient capacity. Coral's Edge TPU enables biometric data to be processed efficiently, without draining the limited power resources available in wearable devices.

All across the US, hospitals are seeking solutions to ensure adherence to hygiene policy amongst hospital staff. In one example, a device incorporates the compact, affordable and offline benefits of the Coral modules to aid in handwashing practices at numerous stations throughout a facility.

And around the world, members of the PyImageSearch community are exploring how to train a COVID-19: Face Mask Detector model using TensorFlow that can be used to identify whether people are wearing a mask. Open source frameworks can empower anyone to develop solutions, and with Coral components we can help bring those benefits to everyone.

Eliciting a global response

In an effort to rally greater community involvement, Coral has joined The United Nations Development Programme and Hackster.io, as a sponsor of the COVID-19 Detect and Protect Challenge. The initiative calls on developers to build affordable and reproducible solutions that support response efforts in developing countries. All ideas are welcome—whether they use ML or not—and we encourage you to participate.

To make edge ML capabilities even easier to integrate, we’re also announcing a price reduction for the Coral products widely used for experimentation and prototyping. Our Dev Board will now be offered at $129.99, the USB Accelerator at $59.99, the Camera Module at $19.99, and the Enviro Board at $14.99. Additionally, we are introducing the USB Accelerator into 10 new markets: Ghana, Thailand, Singapore, Oman, Philippines, Indonesia, Kenya, Malaysia, Israel, and Vietnam. For more details, visit Coral.ai/products.

We’re excited to see the solutions developers will bring forward with Coral. And as always, please keep sending us feedback 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].