RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks

Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda, Kaushik Roy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

69 Scopus citations


Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works were primarily focused on device and circuit implementations of SNNs on crossbars. RESPARC advances this by proposing a complete system for SNN acceleration and its subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs for inner-product computation and realizes a hierarchical reconfigurable design to incorporate the data-flow patterns in an SNN in a scalable fashion. We evaluate the proposed architecture on different SNNs ranging in complexity from 2k-230k neurons and 1.2M-5.5M synapses. Simulation results on these networks show that compared to the baseline digital CMOS architecture, RESPARC achieves 500x (15x) efficiency in energy benefits at 300x (60x) higher throughput for multi-layer perceptrons (deep convolutional networks). Furthermore, RESPARC is a technology-aware architecture that maps a given SNN topology to the most optimized MCA size for the given crossbar technology.

Original languageEnglish (US)
Title of host publicationProceedings of the 54th Annual Design Automation Conference 2017, DAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450349277
StatePublished - Jun 18 2017
Event54th Annual Design Automation Conference, DAC 2017 - Austin, United States
Duration: Jun 18 2017Jun 22 2017

Publication series

NameProceedings - Design Automation Conference
VolumePart 128280
ISSN (Print)0738-100X


Other54th Annual Design Automation Conference, DAC 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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