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Efficient Binary Weight Convolutional Network Accelerator for Speech Recognition.

Lunyi Guo1, Shining Mu1, Yijie Deng1

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient, configurable neural network accelerator for real-time Chinese speech recognition on edge AI devices. It significantly reduces hardware needs and power consumption while maintaining accuracy.

Keywords:
ZYNQbinary weightshardware acceleratormultichannel shared computationspeech recognize

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Area of Science:

  • Artificial Intelligence
  • Computer Engineering
  • Signal Processing

Background:

  • Speech recognition AI has advanced significantly.
  • Real-time offline Chinese speech recognition on edge AI requires performance improvements.

Purpose of the Study:

  • To propose a configurable convolutional neural network accelerator for edge AI speech recognition.
  • To reduce hardware resource consumption and power usage while maintaining accuracy.

Main Methods:

  • Utilized a lightweight speech recognition model with binarized convolutional layer weights.
  • Implemented a multichannel shared computation (MCSC) architecture for data reuse.
  • Designed a binary weight-sharing processing engine (PE) and a custom instruction set.
  • Employed a ping-pong storage method for feature maps.

Main Results:

  • Achieved processing times of 69.8 ms for 2.24s and 189.51 ms for 8s of speech.
  • Reached a convolution performance of 35.66 GOPS/W.
  • Demonstrated superior energy efficiency, lower power consumption, and reduced hardware resource usage compared to other platforms.

Conclusions:

  • The proposed accelerator effectively reduces hardware resource and power consumption for edge AI speech recognition.
  • Binarization and MCSC architecture enhance computational and storage efficiency.
  • The configurable design adapts to various network structures and voice inputs.