tinyML Summit 2022: Challenges for Large Scale Deployment of Tiny ML Devices

inyML Summit 2022
Challenges for Large Scale Deployment of Tiny ML Devices
Gopal RAGHAVAN, Embedded AI Strategy, Microsoft

The last couple of years has shown remarkable progress in extending the limits of ML on Tiny Devices through innovations in device hardware and software. However, this has not resulted in a sizable increase in the number of deployed tiny devices for commercial customers. In this talk, we will examine what we have heard from our commercial customers on edge AI needs and the challenges associated with the large-scale deployment of ML on tiny devices. The highly fragmented nature of this market requires a broad MLDevOps solution supporting a variety of devices and toolchains. We will discuss how Azure and other cloud-based services can offer a solution to this problem and help us achieve the deployment of billions of devices from cloud to the heavy and light edge, all the way to the tiny edge.

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