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.

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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].