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Machine Learning for Microcontroller-Class Hardware: A Review.

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This summary is machine-generated.

This study introduces a specialized workflow for developing machine learning (ML) models for microcontrollers. It addresses memory and compute constraints, enabling onboard ML on resource-limited Internet-of-Things devices.

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Area of Science:

  • Computer Science
  • Embedded Systems
  • Machine Learning

Background:

  • Machine learning (ML) deployment on resource-constrained microcontrollers faces challenges due to high memory and compute requirements.
  • Traditional ML models are often too large for low-power Internet-of-Things (IoT) nodes.

Purpose of the Study:

  • To highlight the unique requirements for enabling onboard ML on microcontroller-class devices.
  • To present a specialized model development workflow for resource-limited applications.
  • To characterize a closed-loop workflow for ML model development on microcontrollers.

Main Methods:

  • A specialized model development workflow tailored for resource-limited applications was employed.
  • The workflow ensures compute and latency budgets are met while maintaining performance.
  • Qualitative and numerical insights were gathered through several use-case demonstrations.

Main Results:

  • The study characterizes a widely applicable, closed-loop workflow for ML on microcontrollers.
  • Several application classes were shown to adopt specific instances of this workflow.
  • Insights into different stages of model development were provided via use cases.

Conclusions:

  • The developed workflow enables effective onboard machine learning for microcontroller devices.
  • Open research challenges and future considerations for ML on microcontrollers were identified.