Machine Learning for Microcontroller-Class Hardware - A Review

• Published in: IEEE sensors journal Vol. 22; no. 22; p. 1
• Main Authors: Saha, Swapnil Sayan, Sandha, Sandeep Singh, Srivastava, Mani
• Format: Journal Article
• Language: English
• Published: United States IEEE 15.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Data models; Feature projection; Hardware; Internet of Things; Machine learning; Mathematical models; Microcontrollers; model compression; neural architecture search; neural networks; optimization; Random access memory; Sensors; TinyML; Workflow; model compression; sensors; optimization; neural networks; Feature projection; internet-of-things; machine learning; TinyML; neural architecture search; microcontrollers
• DOI: 10.1109/JSEN.2022.3210773
• ISSN: 1530-437X

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.