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Achieving Unprecedented Power Savings with Analog ML

Analog computing will be a necessary tool to ensure a future that can support the ML.

www.eetasia.com/, Jan. 30, 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.

Regardless of the specific application, all of these devices share a fundamental reliance on "always-on" ML, as shown in Figure 1.

In other words, these devices continually monitor their environment for some external trigger, such as an audio keyword or anomalous event. Upon detecting this stimulus, the device is triggered into action. In the example of a smart assistant, the device waits to hear the keyword, after which it sends the subsequent audio to the cloud for processing.

Two important factors should be noted about this scheme. First, for the scheme to work, the devices must always be on, constantly sensing the environment for the external trigger that could occur randomly at any time (or never). Second, for the best latency and privacy possible, stimulus detection must be performed on edge.

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