A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems

Giacomo Pedretti, Piergiulio Mannocci, Shahin Hashemkhani, Valerio Milo, Octavian Melnic, Elisabetta Chicca, Daniele Ielmini*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
29 Downloads (Pure)

Abstract

Data-intensive computing applications, such as object recognition, time series prediction, and optimization tasks, are becoming increasingly important in several fields, including smart mobility, health, and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of in-memory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analog values to be used in matrix-vector multiplication (MVM) and their stochasticity that is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This article presents a stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzles by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing.

Original languageEnglish
Article number9086758
Pages (from-to)89-97
Number of pages9
JournalIeee journal on exploratory solid-State computational devices and circuits
Volume6
Issue number1
DOIs
Publication statusPublished - Jun-2020

Keywords

  • Phase change memory (PCM)
  • artificial synapses
  • hopfield neural network
  • stochastic process
  • optimization
  • PART I
  • STATISTICAL FLUCTUATIONS
  • NOISE

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