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Quantization and Deployment of Deep Neural Networks on Microcontrollers.

Pierre-Emmanuel Novac1, Ghouthi Boukli Hacene2,3, Alain Pegatoquet1

  • 1CNRS, LEAT, Université Côte d'Azur, 06903 Sophia Antipolis, France.

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|April 30, 2021
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Summary
This summary is machine-generated.

This study introduces MicroAI, a novel framework for optimizing deep neural networks on low-power microcontrollers. MicroAI enhances efficiency for edge AI applications, offering an alternative to existing embedded inference engines.

Keywords:
artificial intelligenceembedded systemsmachine learningmicrocontrollerspower consumptionquantization

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Area of Science:

  • Embedded Systems
  • Machine Learning
  • Artificial Intelligence

Background:

  • Deploying Artificial Intelligence (AI) on low-power devices presents significant challenges, particularly for deep neural networks (DNNs).
  • Existing solutions often struggle with power consumption, memory limitations, and real-time constraints for edge AI applications.
  • Optimizing DNNs for embedded targets like microcontrollers is crucial for broader adoption in areas like speech recognition and object detection.

Purpose of the Study:

  • To present a new framework, MicroAI, for the end-to-end training, quantization, and deployment of DNNs on low-power 32-bit microcontrollers.
  • To offer an alternative to current embedded inference engines, focusing on ease of adjustment and extensibility for specific use cases.
  • To evaluate the performance of DNNs using various quantization methods and compare MicroAI against established frameworks.

Main Methods:

  • Outlined quantization methods suitable for embedded microcontroller execution.
  • Developed the MicroAI framework supporting single-precision 32-bit floating-point and 8-/16-bit fixed-point integers.
  • Evaluated the framework using UCI-HAR, Spoken MNIST, and GTSRB datasets on ARM Cortex-M4F microcontrollers.

Main Results:

  • Demonstrated the feasibility of deploying DNNs on low-power microcontrollers with the MicroAI framework.
  • Provided a comparative analysis of MicroAI against TensorFlow Lite for Microcontrollers and STM32Cube.AI in terms of memory and power efficiency.
  • Achieved on-device evaluation results on specific ARM Cortex-M4F-based hardware.

Conclusions:

  • MicroAI offers a viable and efficient solution for deploying deep neural networks on resource-constrained embedded devices.
  • The framework's flexibility allows for customization and extension, addressing diverse edge AI requirements.
  • The study highlights the potential for improved power and memory efficiency in embedded AI through advanced quantization and deployment strategies.