Neuromorphic processors with memristive synapses: Synaptic interface and architectural exploration

Qian Wang, Yongtae Kim, Peng Li

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Due to their nonvolatile nature, excellent scalability, and high density, memristive nanodevices provide a promising solution for low-cost on-chip storage. Integrating memristor-based synaptic crossbars into digital neuromorphic processors (DNPs) may facilitate efficient realization of brain-inspired computing. This article investigates architectural design exploration of DNPs with memristive synapses by proposing two synapse readout schemes. The key design tradeoffs involving different analog-to-digital conversions and memory accessing styles are thoroughly investigated. A novel storage strategy optimized for feedforward neural networks is proposed in this work, which greatly reduces the energy and area cost of the memristor array and its peripherals.

Original languageEnglish
Article number35
JournalACM Journal on Emerging Technologies in Computing Systems
Volume12
Issue number4
DOIs
StatePublished - May 2016

Keywords

  • Analog-digital conversion
  • Digital integrated circuits
  • Memristors
  • Neural networks
  • Reconfigurable architectures

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