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Google Builds Custom Processors for Machine Learning

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When AlphaGo, Google's artificial intelligence program, defeated champion Go player Lee Sedol earlier this year, everyone praised its advanced software brain. But the program, developed by Google's DeepMind research team, also had some serious hardware brawn standing behind it. The program was running on custom accelerators that Google's hardware engineers had spent years building in secret, the company said. With the new accelerators plugged into the AlphaGo servers, the program could recognize patterns in its vast library of game data faster than it could with standard processors. The increased speed helped AlphaGo make the kind of quick, intuitive judgments that define how humans approach the game.


Google Builds Custom Processors for Machine Learning

#artificialintelligence

When AlphaGo, Google's artificial intelligence program, defeated champion Go player Lee Sedol earlier this year, everyone praised its advanced software brain. But the program, developed by Google's DeepMind research team, also had some serious hardware brawn standing behind it. The program was running on custom accelerators that Google's hardware engineers had spent years building in secret, the company said. With the new accelerators plugged into AlphaGo's servers, the program could recognize patterns in its vast library of game data faster than it could with standard processors. The increased speed helped AlphaGo make the kind of quick, intuitive judgments that have escaped other computers trying to conquer the game.


Why Low-Power NN Accelerators Matter

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When I released the Speech Commands dataset and code last year, I was hoping they would give a boost to teams building low-energy-usage hardware by providing a realistic application benchmark. It's been great to see Vikas Chandra of ARM using them to build keyword spotting examples for Cortex M-series chips, and now a hardware startup I've been following, Green Waves, have just announced a new device and shared some numbers using the dataset as a benchmark. They're showing power usage numbers of just a few milliwatts for an always-on keyword spotter, which is starting to approach the coin-battery-for-a-year target I think will open up a whole new world of uses. I'm not just excited about this for speech recognition's sake, but because the same hardware can also accelerate vision, and other advanced sensor processing, turning noisy signals into something actionable. I'm also fascinated by the idea that we might be able to build tiny robots with the intelligence of insects if we can get the energy usage and mass small enough, or even send smart nano-probes to nearby stars!


Coral USB Accelerator

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No need to build models from the ground up. Tensorflow Lite models can be compiled to run on USB Accelerator.


Join Asia-Pacific first Legal Services Startup Accelerator Mills Oakley Accelerator

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Mills Oakley and Collective Campus are offering an end-to-end innovation program for new law entrepreneurs and startups, culminating in a 13-week Accelerator that will equip you with the funding, knowledge, tools and support to give your idea the best chance of success.