Microcontroller Runs Neural Networks That Train Themselves

electronicdesign.com, Nov. 05, 2018 – 

Before machine learning algorithms can be used in factories to detect equipment malfunctions or cars to autonomously tell the difference between left and right turn arrows, they need training. That currently takes place in data centers, where neural networks are introduced to hundreds or thousands of examples labeled with what they will need to tell apart. Once trained, the algorithms are programmed into embedded devices to apply their learning.

Eta Compute is trying something different. The company, which has raised more than $10 million in venture capital since it was founded in 2015, announced that its new microcontroller can support spiking neural networks that train themselves. The new Tensai chip can handle unsupervised learning while consuming less power than a hearing aid. That enables the use of continuous processing required in voice interfaces and predictive maintenance.

Nvidia currently dominates the market for machine learning chips. Its graphics processors are the gold standard for training neural networks on massive amounts of data. But Eta Compute is attempting to relocate machine learning to the embedded devices that also collect the data. That would cut down on the communications with the cloud, not only improving latency but also reducing the amount of information that needs cloud storage.