Machine Learning for Microcontroller-Class Hardware: A Review

• Main Authors: Saha, Swapnil Sayan, Sandha, Sandeep Singh, Srivastava, Mani
• Format: Journal Article
• Language: English
• Published: 20-12-2022
• Subjects: Computer Science - Learning
• DOI: 10.48550/arxiv.2205.14550

IEEE Sensors Journal, vol. 22, no. 22, pp. 21362-21390, 15 Nov.,
2022 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
microcontrollers. This paper highlights the unique requirements of enabling
onboard machine learning for microcontroller class devices. Researchers use a
specialized model development workflow for resource-limited applications to
ensure the compute and latency budget is within the device limits while still
maintaining the desired performance. We characterize 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 use cases. Finally,
we identify the open research challenges and unsolved questions demanding
careful considerations moving forward.