Machine Learning for Microcontroller-Class Hardware – A Review
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The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontroller nodes. This paper highlights the unique challenges of enabling onboard machine learning for microcontroller class devices. Recently, researchers have used a specialized model development cycle for resource-limited applications to ensure the compute and latency budget is within the limits while still maintaining the desired accuracy. We introduce a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several applications. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.
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