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Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

Miao Hu1, Catherine E Graves1, Can Li2

  • 1Hewlett Packard Labs, Hewlett Packard Enterprise, Palo Alto, CA, 94304, USA.

Advanced Materials (Deerfield Beach, Fla.)
|January 11, 2018
PubMed
Summary

This study demonstrates high-precision analog tuning of memristor crossbar arrays for deep neural network acceleration. The memristor system achieved 6-bit VMM accuracy and 89.9% MNIST recognition, paving the way for efficient AI hardware.

Keywords:
crossbar arraysmemristormetal oxideneuromorphic computing

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Memristor crossbar arrays offer a promising hardware acceleration for deep neural networks.
  • Current limitations include materials and device challenges hindering large-scale, reliable analog programming.

Purpose of the Study:

  • To demonstrate high-precision analog tuning and control of memristor cells in a large-scale array.
  • To evaluate the vector-matrix multiplication (VMM) computing precision and neural network inference performance.

Main Methods:

  • Utilized a 128x64 memristor crossbar array for analog computation.
  • Performed single-layer neural network inference and compared performance against digital methods.

Main Results:

  • Achieved high-precision analog tuning across the entire 128x64 array.
  • The memristor computing system demonstrated a VMM accuracy equivalent to 6 bits.
  • Attained 89.9% recognition accuracy on the 10k MNIST handwritten digit dataset.

Conclusions:

  • Successfully demonstrated reliable analog programming in large memristor arrays for AI acceleration.
  • The results show memristor computing's potential for high efficiency, exceeding 100 trillion operations per second per Watt with scaled integration.
  • This work overcomes key device limitations for practical memristor-based neural network hardware.