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Energy-Efficient Hardware Implementation of Fully Connected Artificial Neural Networks Using Approximate Arithmetic

Mohammadreza Esmali Nojehdeh1, Mustafa Altun1

  • 1Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey.

Circuits, Systems, and Signal Processing
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces efficient hardware for artificial neural networks (ANNs) using approximate computing. Approximate adders and multipliers significantly reduce area and energy consumption in ANNs.

Keywords:
Approximate adderApproximate multiplierArtificial neural network (ANN)Multiply accumulate (MAC)

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence Hardware

Background:

  • Artificial Neural Networks (ANNs) demand significant hardware resources, especially in parallel architectures.
  • Time-multiplexed architectures are used to manage the area requirements of ANNs by reusing computing resources in Multiply-Accumulate (MAC) blocks.

Purpose of the Study:

  • To explore efficient hardware implementation of feedforward ANNs using approximate adders and multipliers.
  • To develop an algorithm for determining the appropriate level of approximation for multipliers and adders based on desired accuracy.
  • To evaluate the impact of approximate computing on ANN hardware efficiency.

Main Methods:

  • Implemented ANNs using a time-multiplexed architecture with approximate adders and multipliers in MAC blocks.
  • Developed and applied an algorithm to set the approximation levels for hardware components.
  • Tested the proposed methods on MNIST and SVHN datasets using various ANN architectures.

Main Results:

  • ANNs designed with the proposed approximate multiplier showed reduced area and energy consumption compared to existing approximate multipliers.
  • Utilizing both approximate adders and multipliers led to up to 50% reduction in energy consumption and 10% reduction in area.
  • The designs maintained high hardware accuracy with minimal deviation from exact computations.

Conclusions:

  • Approximate computing offers a viable strategy for enhancing the efficiency of ANN hardware implementations.
  • The proposed method effectively reduces the area and energy footprint of ANNs without substantial accuracy loss.
  • This approach is beneficial for deploying ANNs in resource-constrained environments.