The aptly named software, which greatly reduces the amount of circuitry needed to perform autonomous tasks, is expected to increase the penetration of artificial intelligence into markets for mobile phones, self-driving cars and automated interpretation of images.
“Instead of sending out endless energy dribbles of information,” Sandia neuroscientist Brad Aimone said, “artificial neurons trained by Whetstone release energy in spikes, much like human neurons do.”
The largest artificial intelligence companies have produced spiking tools for their own products, but none are as fast or efficient as Whetstone, says Sandia mathematician William Severa. “Large companies are aware of this process and have built similar systems, but often theirs work only for their own designs. Whetstone will work on many neural platforms.”
The open-source code was recently featured in a technical article in Nature Machine Intelligence and has been proposed by Sandia for a patent.
How to sharpen neurons
Artificial neurons are basically capacitors that absorb and sum electrical charges they then release in tiny bursts of electricity. Computer chips, termed “neuromorphic systems,” assemble neural networks into large groupings that mimic the human brain by sending electrical stimuli to neurons firing in no predictable order. This contrasts with a more lock-step procedure used by desktop computers with their pre-set electronic processes.
Because of their haphazard firing, neuromorphic systems often are slower than conventional computers but also require far less energy to operate. They also require a different approach to programming because otherwise their artificial neurons fire too often or not often enough, which has been a problem in bringing them online commercially.