Why Low-Power NN Accelerators Matter


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!

Neural networks offer a whole new way of programming that’s inherently a lot easier to scale down than conventional instruction-driven approaches. You can transform and convert network models in ways we’ve barely begun to explore, fitting them to hardware with few resources while preserving performance. Chips can also take a lot of shortcuts that aren’t possible with traditional code, like tolerating calculation errors, and they don’t have to worry about awkward constructs like branches, everything is straight-line math at its heart.

I’ve put in my preorder for a GAP8 developer kit, to join the ARM-based prototyping devices on my desk, and I’m excited to see so much activity in this area. I think we’re going to see a lot of progress over the next couple of years, and I can’t wait to see what new applications emerge as hardware capabilities keep improving!

Blue Pill: A 72MHz 32-Bit Computer for $2!


Some people love tiny houses, but I’m fascinated by tiny computers. My house is littered with Raspberry Pi’s, but recently my friend Andy Selle introduced me to Blue Pill single-board computers. These are ARM M3 CPUs running at 72MHz, available for $2 or less on Ebay and Aliexpress, even when priced individually. These are complete computers with 20KB of RAM and 64KB of Flash for programs, and while that may not sound like much memory, their computing power as 32-bit ARM CPUs running at a fast clock-rate make them very attractive for applications like machine learning that rely more on arithmetic than memory. Even better, they can run for weeks or months on a single battery thanks to their ultra-low energy usage.

This makes them interesting platforms to explore the emerging world of smart sensors; they may not quite be fifty cents each, but they’re in the same ballpark. Unfortunately I’m a complete novice when it comes to microcontrollers, but luckily Andy was able to give me a few pointers to help me get started. After I struggled through a few hurdles, I managed to get a workflow laid out that I like, and ran some basic examples. To leave a trail of breadcrumbs for anyone else who’s fascinated by the possibilities of these devices, I’ve open-sourced stm32_bare_lib on GitHub. It includes step by step instructions designed for a newbie like me, especially on the wiring (which doesn’t require soldering or any special tools, thankfully), and has some examples written in plain C to play with. I hope you have as much fun playing with these tiny computers as I have!