Machine Learning for Microcontroller-Class Hardware: A Review
It addresses the problem of enabling onboard intelligence for low-end IoT devices, but it is incremental as it synthesizes existing approaches rather than introducing new methods.
This paper reviews the challenges and workflows for deploying machine learning on resource-constrained microcontrollers, highlighting specialized development processes to meet compute and latency limits while maintaining performance.
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.