New Tools for Edge Computing
SparkFun is pleased to announce a new line of development tools to help implement edge computing, including voice and image recognition.
Academia has been talking about artificial intelligence for decades, and while the field has made amazing strides, there haven’t been many tangible effects for general users. With exciting new tools like TensorFlow for machine learning, and increasing processing power, we are starting to see improvements that we can begin to reap benefits from.
Introducing the SparkFun Edge!
I’m pleased to announce the SparkFun Edge. Powered by the Apollo3 from Ambiq, the SparkFun Edge allows us to finally poke around with edge computing and implement actual uses such as voice and image recognition.
But wait you say, I’ve got a voice thing in my home I use all the time. How’s this different?
The major difference is that no internet is required. All the processing is done on our board, at the edge of your project. Alexa, Siri and Google Home are always listening, and always uploading audio data to the web for analysis. There are obvious privacy concerns, and some not so obvious global warming concerns with these always-on, always-connected devices. The future is in lower-power, smaller, more specialized assistive devices with limited or no connection to the internet. This is where the SparkFun Edge shines. It cannot order you a dozen eggs from Whole Paycheck, but the SparkFun Edge can activate a blender or hot-water heater, or do anything your microcontroller can do when it hears a certain word, with nothing but a coin cell battery for power. How? I'm so glad you asked!
At the heart of the SparkFun Edge is the Apollo3, the latest Cortex M4 from Ambiq Micro. Consuming just 8uA per MHz, this processor is possibly the lowest power microcontroller on the planet. It's so low, in fact, that we’ve demonstrated voice recognition using nothing but a coin cell battery. Try that, Siri. Additionally, the Apollo3 has built in BLE, 50 GPIO, a MB of flash, 384k of SRAM, and runs at 96MHz using less than 1mA at full tilt. With power cycling, the Edge can perform for days or weeks on nothing but a 3V CR2032. With tools like TensorFlow Lite, the incredibly complex world of machine learning can now be squeezed onto the SparkFun Edge. We’re pretty excited.
We’ve been working with a team at Google to implement TensorFlow Lite for audio recognition, and have promising results. We’ve proven out the hardware including the microphones, accelerometer and camera interface, so we’re making the Edge available for pre-order today. We have documentation on how to set up and use the Ambiq SDK to implement the voice recognition demo, and hope to release image recognition examples soon. We’re also working on an Arduino port that should be available in the next few months. If you’re interested in playing with TensorFlow on embedded targets, or if you just want a wickedly powerful micro to replace your Uno, you should consider checking out the SparkFun Edge today.
