Posted by The Coral Team
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
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
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 coral-support@google.com.
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.
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.
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.
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.
We’ve also made a number of new updates to our ML tools:
sudo apt-get update && sudo apt-get install edgetpu
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.
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.
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 coral-support@google.com.
Posted by Billy Rutledge, Director of the Coral team
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.
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.
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.
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 coral-support@google.com.
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.
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.
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.
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 coral-support@google.com.
Posted by Vikram Tank (Product Manager), Coral Team
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 coral-support@google.com.