ASTERS: Adaptable Threshold Spike-timing Neuromorphic Design with Twin-Column ReRAM Synapses

Conference Paper

Complex event-driven neuron dynamics was an obstacle to implementing efficient brain-inspired computing architectures with VLSI circuits. To solve this problem and harness the event-driven advantage, we propose ASTERS, a resistive random-access memory (ReRAM) based neuromorphic design to conduct the time-to-first-spike SNN inference. In addition to the fundamental novel axon and neuron circuits, we also propose two techniques through hardware-software co-design: "Multi-Level Firing Threshold Adjustment"to mitigate the impact of ReRAM device process variations, and "Timing Threshold Adjustment"to further speed up the computation. Experimental results show that our cross-layer solution ASTERS achieves more than 34.7% energy savings compared to the existing spiking neuromorphic designs, meanwhile maintaining 90.1% accuracy under the process variations with a 20% standard deviation.

Full Text

Duke Authors

Cited Authors

  • Li, Z; Zheng, Q; Yan, B; Huang, R; Li, B; Chen, Y

Published Date

  • July 10, 2022

Published In

Start / End Page

  • 1099 - 1104

International Standard Serial Number (ISSN)

  • 0738-100X

International Standard Book Number 13 (ISBN-13)

  • 9781450391429

Digital Object Identifier (DOI)

  • 10.1145/3489517.3530591

Citation Source

  • Scopus