SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM

Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique

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

Abstract

Spiking Neural Networks (SNNs) have the potential for achieving low energy consumption due to their biologically sparse computation. Several studies have shown that the off-chip memory (DRAM) accesses are the most energy-consuming operations in SNN processing. However, state-of-the-art in SNN systems do not optimize the DRAM energy-per-access, thereby hindering achieving high energy-efficiency. To substantially minimize the DRAM energy-per-access, a key knob is to reduce the DRAM supply voltage but this may lead to DRAM errors (i.e., the so-called approximate DRAM). Towards this, we propose SparkXD, a novel framework that provides a comprehensive conjoint solution for resilient and energy-efficient SNN inference using low-power DRAMs subjected to voltage-induced errors. The key mechanisms of SparkXD are: (1) improving the SNN error tolerance through fault-aware training that considers bit errors from approximate DRAM, (2) analyzing the error tolerance of the improved SNN model to find the maximum tolerable bit error rate (BER) that meets the targeted accuracy constraint, and (3) energy-efficient DRAM data mapping for the resilient SNN model that maps the weights in the appropriate DRAM location to minimize the DRAM access energy. Through these mechanisms, SparkXD mitigates the negative impact of DRAM (approximation) errors, and provides the required accuracy. The experimental results show that, for a target accuracy within 1% of the baseline design (i.e., SNN without DRAM errors), SparkXD reduces the DRAM energy by ca. 40% on average across different network sizes.

Original languageEnglish (US)
Title of host publication2021 58th ACM/IEEE Design Automation Conference, DAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages379-384
Number of pages6
ISBN (Electronic)9781665432740
DOIs
StatePublished - Dec 5 2021
Event58th ACM/IEEE Design Automation Conference, DAC 2021 - San Francisco, United States
Duration: Dec 5 2021Dec 9 2021

Publication series

NameProceedings - Design Automation Conference
Volume2021-December
ISSN (Print)0738-100X

Conference

Conference58th ACM/IEEE Design Automation Conference, DAC 2021
Country/TerritoryUnited States
CitySan Francisco
Period12/5/2112/9/21

Keywords

  • approximate computing
  • DRAM errors
  • efficiency
  • energy
  • error-tolerance
  • inference
  • resilience
  • Spiking neural networks

ASJC Scopus subject areas

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

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