Radiation Effects in Brain-Inspired Computing Systems

Abstract

This work explores the effects of radiation in digital and analog hardware implementations of brain-inspired computing systems. The IBM TrueNorth Neurosynaptic System was utilized as a baseline system to evaluate the effects of radiation on neural network applications emulated by von Neumann hardware. The IBM TrueNorth was irradiated using 4 MeV proton irradiation at the Vanderbilt Pelletron. The effects of Single-Event Upsets (SEUs) on network accuracy and performance were negligible, reducing the accuracy of an MNIST-trained Convolutional Neural Network (CNN) with trinary weights from98.82 to 98.78 after a 60 second exposure. Secondly, we also studied the effects of radiation on general analog in-mem

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2021
Accession Number
AD1143545

Entities

People

  • Brian D. Sierawski
  • Jason Woo
  • Michael Alles
  • Subramanian Iyer

Organizations

  • University of California, Los Angeles
  • Vanderbilt University

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Accuracy
  • Artificial Intelligence Software
  • Computer Programming
  • Computing System Architectures
  • Convolutional Neural Networks
  • Energy Bands
  • Experimental Data
  • Memory Devices
  • Network Architecture
  • Neural Networks
  • Radiation Effects
  • Reliability
  • Semiconductor Devices
  • Semiconductors
  • Simulations
  • Simulators
  • X Rays

Fields of Study

  • Physics

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.
  • Nuclear and Radiation Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks