Sparse Random Signals for Fast Convergence on Invertible Logic

Naoya Onizawa, Makoto Kato, Hitoshi Yamagata, Koji Yano, Seiichi Shin, Hiroyuki Fujita, Takahiro Hanyu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper introduces sparse random signals for fast convergence on invertible logic. Invertible logic based on a network of probabilistic nodes (spins), similar to a Boltzmann machine, can compute functions bidirectionally by reducing the network energy to the global minimum with the addition of random signals. Here, we propose using sparse random signals that are generated by replacing a part of the typical dense random signals with zero values in probability. The sparsity of the random signals can induce a relatively relaxed transition of the spin network, reaching the global minimum energy at high probabilities. As a typical design example of invertible logic, invertible adders and multipliers are designed and evaluated. The simulation results show that the convergence speed with the proposed sparse random signals is roughly an order of magnitude faster than that with the conventional dense random signals. In addition, several key parameters are found and could be a guideline for fast convergence on general invertible logic.

Original languageEnglish
Article number9399449
Pages (from-to)62890-62898
Number of pages9
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Boltzmann machine
  • Stochastic computing
  • bidirectional operations

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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