Posted by Jennifer Kohl, Program Manager, Developer Community Programs
On October 16-18, thousands of developers from all over the world are coming together for DevFest 2020, the largest virtual weekend of community-led learning on Google technologies.
As people around the world continue to adapt to spending more time at home, developers yearn for community now more than ever. In years past, DevFest was a series of in-person events over a season. For 2020, the community is coming together in a whole new way – virtually – over one weekend to keep developers connected when they may want it the most.
The magic of DevFest comes from the people who organize and speak at the events - developers with various backgrounds and skill levels, all with their own unique perspectives. In different parts of the world, you can find a DevFest session in many local languages. DevFest speakers are made up of various types of technologists, including kid developers , self-taught programmers from rural areas , and CEOs and CTOs of startups. DevFest also features a wide range of speakers from Google, Women Techmakers, Google Developer Experts, and more. Together, these friendly faces, with many different perspectives, create a unique and rich developer conference.
Hosted by Google Developer Groups, this year’s sessions include technical talks and workshops from the community, and a keynote from Google Developers. Through these events, developers will learn how Google technologies help them develop, learn, and build together.
Sessions will cover multiple technologies, such as Android, Google Cloud Platform, Machine Learning with TensorFlow, Web.dev, Firebase, Google Assistant, and Flutter.
For this reason, Google Developers supports the community-led efforts of Google Developer Groups and their annual tentpole event, DevFest. Google provides esteemed speakers from the company and custom technical content produced by developers at Google. The impact of DevFest is really driven by the grassroots, passionate GDG community organizers who volunteer their time. Google Developers is proud to support them.
During DevFest 2019, 138,000+ developers participated across 500+ DevFests in 100 countries. While 2020 is a very different year for events around the world, GDG chapters are galvanizing their communities to come together virtually for this global moment. The excitement for DevFest continues as more people seek new opportunities to meet and collaborate with like-minded, community-oriented developers in our local towns and regions.
Join the conversation on social media with #DevFest.
Sign up for DevFest at goo.gle/devfest.
Still curious? Check out these popular talks from DevFest 2019 events around the world...
Scalable Vector Graphics (SVG)
How to add Flutter to your app
The Flutter journey: production app
Getting started with microcontrollers
Chet Haase and Romaine Guy: quality
Building the ultimate Beatles app
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.
Posted by Zhicheng Wang and Genzhi Ye, MediaPipe team
In many computer vision applications, a crucial building block is to establish reliable correspondences between different views of an object or scene, forming the foundation for approaches like template matching, image retrieval and structure from motion. Correspondences are usually computed by extracting distinctive view-invariant features such as SIFT or ORB from images. The ability to reliably establish such correspondences enables applications like image stitching to create panoramas or template matching for object recognition in videos (see Figure 1).
Today, we are announcing KNIFT (Keypoint Neural Invariant Feature Transform), a general purpose local feature descriptor similar to SIFT or ORB. Likewise, KNIFT is also a compact vector representation of local image patches that is invariant to uniform scaling, orientation, and illumination changes. However unlike SIFT or ORB, which were engineered with heuristics, KNIFT is an embedding learned directly from a large number of corresponding local patches extracted from nearby video frames. This data driven approach implicitly encodes complex, real-world spatial transformations and lighting changes in the embedding. As a result, the KNIFT feature descriptor appears to be more robust, not only to affine distortions, but to some degree of perspective distortions as well. We are releasing an implementation of KNIFT in MediaPipe and a KNIFT-based template matching demo in the next section to get you started.
Please note that the information, uses, and applications expressed in the below post are solely those of our guest author, Alfred Camera.
In this article, we’d like to give you a short overview of Alfred Camera and our experience of using MediaPipe to transform our moving object feature, and how MediaPipe has helped to get things easier to achieve our goals.
Fig.1 Alfred Camera Logo
Alfred Camera is a smart home app for both Android and iOS devices, with over 15 million downloads worldwide. By downloading the app, users are able to turn their spare phones into security cameras and monitors directly, which allows them to watch their homes, shops, pets anytime. The mission of Alfred Camera is to provide affordable home security so that everyone can find peace of mind in this busy world.
The Alfred Camera team is composed of professionals in various fields, including an engineering team with several machine learning and computer vision experts. Our aim is to integrate AI technology into devices that are accessible to everyone.
Alfred Camera currently has a feature called Moving Object Detection, which continuously uses the device’s camera to monitor a target scene. Once it identifies a moving object in the area, the app will begin recording the video and send notifications to the device owner. The machine learning models for detection are hand-crafted and trained by our team using TensorFlow, and run on TensorFlow Lite with good performance even on mid-tier devices. This is important because the app is leveraging old phones and we'd like the feature to reach as many users as possible.
We had started building our AI features at Alfred Camera since 2017. In order to have a solid foundation to support our AI feature requirements for the coming years, we decided to rebuild our real-time video analysis pipeline. At the beginning of the project, the goals were to create a new pipeline which should be 1) modular enough so we could swap core algorithms easily with minimal changes in other parts of the pipeline, 2) having GPU acceleration designed in place, 3) cross-platform as much as possible so there’s no need to create/maintain separate implementations for different platforms. Based on the goals, we had surveyed several open source projects that had the potential but we ended up using none of them as they either fell short on the features or were not providing the readiness/stabilities that we were looking for.
We started a small team to prototype on those goals first for the Android platform. What came later were some tough challenges way above what we originally anticipated. We ran into several major design changes as some key design basics were overlooked. We needed to implement some utilities to do things that sounded trivial but required significant effort to make it right and fast. Dealing with asynchronous processing also led us into a bunch of timing issues, which took the team quite some effort to address. Not to mention debugging on real devices was extremely inefficient and painful.
Things didn't just stop here. Our product is also on iOS and we had to tackle these challenges once again. Moreover, discrepancies in the behavior between the platform-specific implementations introduced additional issues that we needed to resolve.
Even though we finally managed to get the implementations to the confidence level we wanted, that was not a very pleasant experience and we have never stopped thinking if there is a better option.
Google open sourced MediaPipe project in June 2019 and it immediately caught our attention. We were surprised by how it is perfectly aligned with the previous goals we set, and has functionalities that could not have been developed with the amount of engineering resources we had as a small company.
We immediately decided to start an evaluation project by building a new product feature directly using MediaPipe to see if it could live up to all the promises.
To start the evaluation, we decided to migrate our existing moving object feature to see what exactly MediaPipe can do.
Our current Moving Object Detection pipeline consists of the following main components:
Our current implementation has taken GPU into consideration as much as we can. A series of shaders are created to perform the tasks above and the pipeline is designed to avoid moving pixels between CPU/GPU frequently to eliminate the potential performance hits.
The pipeline involves multiple ML models that are conditionally executed, mixed CPU/GPU processing, etc. All the challenges here make it a perfect showcase for how MediaPipe could help develop a complicated pipeline.
MediaPipe provides a lot of code samples for any developer to bootstrap with. We took the Object Detection on Android sample that comes with the project to start with because of the similarity with the back-end part of our pipeline. It did take us sometimes to fully understand the design concepts of MediaPipe and all the tools associated. But with the complete documentation and the great responsiveness from the MediaPipe team, we got up to speed soon to do most of the things we wanted.
That being said, there were a few challenges we needed to overcome on the road to full migration. Our original pipeline of Moving Object Detection takes the input frame asynchronously, but MediaPipe has timestamp bound limitations such that we cannot just show the result in an allochronic way. Meanwhile, we need to gather data through JNI in a specific data format. We came up with a workaround that conquered all the issues under the circumstances, which will be mentioned later.
After wrapping our models and the processing logics into calculators and wired them up, we have successfully transformed our existing implementation and created our first MediaPipe Moving Object Detection pipeline like the figure below, running on Android devices:
Fig.2 Moving Object Detection Graph
We do not block the video frame in the main calculation loop, and set the detection result as an input stream to show the annotation on the screen. The whole graph is designed as a multi-functioned process, the left chunk is the debug annotation and video frame output module, and the rest of the calculation occurs in the rest of the graph, e.g., low light detection, motion triggered detection, cropping of the area of interest and the detection process. In this way, the graph process will naturally separate into real-time display and asynchronous calculation.
As a result, we are able to complete a full processing for detection in under 40ms on a device with Snapdragon 660 chipset. MediaPipe’s tight integration with TensorFlow Lite provides us the flexibility to get even more performance gain by leveraging whatever acceleration techniques available (GPU or DSP) on the device.
The following figure shows the current implementation working in action:
Fig.3 Moving Object Detection running in Alfred Camera
After getting things to run on Android, Desktop GPU (OpenGL-ES) emulation was our next target to evaluate. We are already using OpenGL-ES shaders for some computer vision operations in our pipeline. Having the capability to develop the algorithm on desktop, seeing it work in action before deployment onto mobile platforms is a huge benefit to us. The feature was not ready at the time when the project was first released, but MediaPipe team had soon added Desktop GPU emulation support for Linux in follow-up releases to make this possible. We have used the capability to detect and fix some issues in the graphs we created even before we put things on the mobile devices. Although it currently only works on Linux, it is still a big leap forward for us.
Testing the algorithms and making sure they behave as expected is also a challenge for a camera application. MediaPipe helps us simplify this by using pre-recorded MP4 files as input so we could verify the behavior simply by replaying the files. There is also built-in profiling support that makes it easy for us to locate potential performance bottlenecks.
The result of the evaluation and the feedback from our engineering team were very positive and promising:
With just a few weeks working with MediaPipe, it has shown strong capabilities to fundamentally transform how we develop our products. Without MediaPipe we could have spent months creating the same features without the same level of performance.
Alfred Camera is designed to bring home security with AI to everyone, and MediaPipe has significantly made achieving that goal easier for our team. From Moving Object Detection to future AI-powered features, we are focusing on transforming a basic security camera use case into a smart housekeeper that can help provide even more context that our users care about. With the support of MediaPipe, we have been able to accelerate our development process and bring the features to the market at an unprecedented speed. Our team is really excited about how MediaPipe could help us progress and discover new possibilities, and is looking forward to the enhancements that are yet to come to the project.
MediaPipe is a framework for building cross-platform multimodal applied ML pipelines. We have previously demonstrated building and running ML pipelines as MediaPipe graphs on mobile (Android, iOS) and on edge devices like Google Coral. In this article, we are excited to present MediaPipe graphs running live in the web browser, enabled by WebAssembly and accelerated by XNNPack ML Inference Library. By integrating this preview functionality into our web-based Visualizer tool, we provide a playground for quickly iterating over a graph design. Since everything runs directly in the browser, video never leaves the user’s computer and each iteration can be immediately tested on a live webcam stream (and soon, arbitrary video).
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.
Research shows the potential impact of FAW on continental wide maize yield lies between 8.3 and 20.6 million tonnes per year (total expected production of 39m tonnes per year); with losses lying between US$2,48m and US$6,19m per year (of a US$11,59m annual expected value). The impact of FAW is far reaching, and is now reported in many countries around the world.
Agriculture is the backbone of Uganda’s economy, employing 70% of the population. It contributes to half of Uganda’s export earnings and a quarter of the country’s gross domestic product (GDP). Fall armyworm posses a great threat on our livelihoods. We are a small group of like minded developers living and working in Uganda. Most of our relatives grow maize so the impact of the worm was very close to home. We really felt like we needed to do something about it. The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help. It is the perfect time to apply machine learning. Our goal is to build an intelligent agent to help local farmers fight this pest in order to increase our food security.
Based on a Machine Learning Crash Course, our Google Developer Group (GDG) in Mbale hosted some study jams in May 2018, alongside several other code labs. This is where we first got hands-on experience using TensorFlow, from which the foundations were laid for the Farmers Companion app. Finally, we felt as though an intelligent solution to help farmers had been conceived.
Equipped with this knowledge & belief, the team embarked on collecting training data from nearby fields. This was done using a smartphone to take images, with the help of some GDG Mbale members. With farmers miles from town, and many fields inaccessible by road (not to mention the floods), this was not as simple as we had first hoped. To inhibit us further, our smartphones were (and still are) the only hard drives we had, thus decreasing the number of images & data we can capture in a day.
But we persisted! Once gathered, the images were sorted, one at a time, and categorized. With TensorFlow we re-trained a MobileNet, a technique known as transfer learning. We then used the TensorFlow Converter to generate a TensorFlow Lite FlatButter file which we deployed in an Android app. We started with about 3956 images, but our dataset is growing exponentially. We are actively collecting more and more data to improve our model’s accuracy. The improvements in TensorFlow, with Keras high level APIs, has really made our approach to deep learning easy and enjoyable and we are now experimenting with TensorFlow 2.0.
The app is simple for the user. Once installed, the user focuses the camera through the app, on a maize crop. Then an image frame is picked and, using TensorFlow Lite, the image frame is analysed to look for Fall armyworm damage. Depending on the results from this phase, a suggestion of a possible solution is given.
The app is available for download and it is constantly undergoing updates, as we push for local farmers to adapt and use it. We strive to ensure a world with #ZeroHunger and believe technology can do a lot to help us achieve this.
We have so far been featured on a national TV station in Uganda, participated in the #hackAgainstHunger and ‘The International Symposium on Agricultural Innovations’ for family farmers, organized by the Food Agricultural Organization of the United Nations, where our solution was highlighted.
We have embarked on scaling the solution to coffee disease and cassava diseases and will slowly be moving on to more. We have also introduced virtual reality to help farmers showcase good farming practices and various training.
Our plan is to collect more data and to scale the solution to handle more pests and diseases. We are also shifting to cloud services and Firebase to improve and serve our model better despite the lack of resources. With improved hardware and greater localised understanding, there's huge scope for Machine Learning to make a difference in the fight against hunger.
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.
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.
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.
You can see the full release notes on Coral site.
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.
Posted by Andrew Zaldivar, Developer Advocate, Google AI
A few months ago, we announced our AI Principles, a set of commitments we are upholding to guide our work in artificial intelligence (AI) going forward. Along with our AI Principles, we shared a set of recommended practices to help the larger community design and build responsible AI systems.
In particular, one of our AI Principles speaks to the importance of recognizing that AI algorithms and datasets are the product of the environment—and, as such, we need to be conscious of any potential unfair outcomes generated by an AI system and the risk it poses across cultures and societies. A recommended practice here for practitioners is to understand the limitations of their algorithm and datasets—but this is a problem that is far from solved.
To help practitioners take on the challenge of building fairer and more inclusive AI systems, we developed a short, self-study training module on fairness in machine learning. This new module is part of our Machine Learning Crash Course, which we highly recommend taking first—unless you know machine learning really well, in which case you can jump right into the Fairness module.
The Fairness module features a hands-on technical exercise. This exercise demonstrates how you can use tools and techniques that may already exist in your development stack (such as Facets Dive, Seaborn, pandas, scikit-learn and TensorFlow Estimators to name a few) to explore and discover ways to make your machine learning system fairer and more inclusive. We created our exercise in a Colaboratory notebook, which you are more than welcome to use, modify and distribute for your own purposes.
From exploring datasets to analyzing model performance, it's really easy to forget to make time for responsible reflection when building an AI system. So rather than having you run every code cell in sequential order without pause, we added what we call FairAware tasks throughout the exercise. FairAware tasks help you zoom in and out of the problem space. That way, you can remind yourself of the big picture: finding the undesirable biases that could disproportionately affect model performance across groups. We hope a process like FairAware will become part of your workflow, helping you find opportunities for inclusion.
FairAware task guiding practitioner to compare performances across gender.
The Fairness module was created to provide you with enough of an understanding to get started in addressing fairness and inclusion in AI. Keep an eye on this space for future work as this is only the beginning.
If you wish to learn more from our other examples, check out the Fairness section of our Responsible AI Practices guide. There, you will find a full set of Google recommendations and resources. From our latest research proposal on reporting model performance with fairness and inclusion considerations, to our recently launched diagnostic tool that lets anyone investigate trained models for fairness, our resource guide highlights many areas of research and development in fairness.
Let us know what your thoughts are on our Fairness module. If you have any specific comments on the notebook exercise itself, then feel free to leave a comment on our GitHub repo.
On behalf of many contributors and supporters,
Andrew Zaldivar – Developer Advocate, Google AI
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.
