SalvagedNn: Salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping

Muhammad Abdullah Hanif, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review

Abstract

Deep neural networks (DNNs) have proliferated in most of the application domains that involve data processing, predictive analysis and knowledge inference. Alongside the need for developing highly performance-efficient DNN accelerators, there is an utmost need to improve the yield of the manufacturing process in order to reduce the per unit cost of the DNN accelerators. To this end, we present ‘SalvageDNN’, a methodology to enable reliable execution of DNNs on the hardware accelerators with permanent faults (typically due to imperfect manufacturing processes). It employs a fault-aware mapping of different parts of a given DNN on the hardware accelerator (subjected to faults) by leveraging the saliency of the DNN parameters and the fault map of the underlying processing hardware. We also present novel modifications in a systolic array design to further improve the yield of the accelerators while ensuring reliable DNN execution using ‘SalvageDNN’ and negligible overheads in terms of area, power/energy and performance.

Original languageEnglish (US)
Article number20190164
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume378
Issue number2164
DOIs
StatePublished - Feb 7 2020

Keywords

  • Accelerators
  • Energy-efficiency
  • Hardware
  • Neural networks
  • Reliability
  • Reliable computing

ASJC Scopus subject areas

  • Mathematics(all)
  • Engineering(all)
  • Physics and Astronomy(all)

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