Industry Articles
Achieving Unprecedented Power Savings with Analog ML-January 16, 2023 |
By Tom Doyle, Aspinitiy
EETimes (January 12, 2023)
The rise of machine learning (ML) has enabled an entirely new class of use cases and applications. Specifically, edge computing and on-edge ML have augmented traditional devices with the ability to monitor, analyze and automate daily tasks.
Despite these advances, a major challenge remains: How do you balance the high-power demands of these ML applications with the low-power requirements of standalone, battery-powered devices? For these applications, traditional digital electronics are no longer the best option. Analog computing has emerged as the obvious choice to achieve ultra-low-power ML on the edge.
With the advent of on-edge ML, the industry has seen a proliferation of smart devices that respond to stimuli in the environment. Many households today, for example, host a virtual assistant like Amazon Alexa or Google Home that listens for a keyword before performing a task. Other examples include security cameras that monitor for movement in a frame and, on the industrial side, sensors that detect anomalies in the performance of an industrial machine.