Machine Learning for Microcontroller-Class Hardware: A Review

The advancements in machine learning (ML) opened a new opportunity to bring intelligence to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML deployment has high memory and computes footprint hindering their direct deployment on ultraresource-constrained microcontrollers. This article highlights the unique requirements of enabling onboard ML for microcontroller-class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure that 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 ML 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.

• Publication year

  2022

• Venue

  IEEE Sensors Journal

• Publication date

  2022-05-29

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1109/JSEN.2022.3210773arXiv 2205.14550PMID 36439060PMCID PMC9683383
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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