Memristive devices for spiking neural networks

Bipin Rajendran, Damien Querlioz, Sabina Spiga, Abu Sebastian

Research output: Chapter in Book/Report/Conference proceedingChapter


Spiking neural networks (SNNs) are artificial learning models that closely mimic the time-based information encoding and processing mechanisms observed in the brain. As opposed to deep learning models that use real numbers for information encoding, SNNs use binary spike signals and their arrival times to encode information, which could potentially improve the algorithmic efficiency of computation. However overall system efficiency improvement for learning and inference systems implementing SNNs will depend on the ability to reduce data movement between processor and memory units, and hence in-memory computing architectures employing nanoscale memristive devices that operate at low power would be essential. The requirements and specifications for these devices for realizing SNNs are quite different from those of regular deep learning models. In this chapter we introduce some of the fundamental aspects of spike-based information processing and how nanoscale memristive devices could be used to efficiently implement these algorithms for cognitive applications.

Original languageEnglish (US)
Title of host publicationMemristive Devices for Brain-Inspired Computing
Subtitle of host publicationFrom Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks
Number of pages7
ISBN (Electronic)9780081027820
StatePublished - Jan 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Engineering


  • In-memory computing
  • Memristor
  • Spiking neural network


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