DL-RSIM: A Reliability and Deployment Strategy Simulation Framework for ReRAM-based CNN Accelerators

Wei Ting Lin, Hsiang Yun Cheng, Chia Lin Yang, Meng Yao Lin, Kai Lien, Han Wen Hu, Hung Sheng Chang, Hsiang Pang Li, Meng Fan Chang, Yen Ting Tsou, Chin Fu Nien

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. In addition, due to the hardware constraints, the way to deploy neural network models on memristor crossbar arrays affects the computation parallelism and communication overheads. To enable reliable and energy-efficient memristor-based accelerators, a simulation platform is needed to precisely analyze the impact of non-ideal circuit/device properties on the inference accuracy and the influence of different deployment strategies on performance and energy consumption. In this paper, we propose a flexible simulation framework, DL-RSIM, to tackle this challenge. A rich set of reliability impact factors and deployment strategies are explored by DL-RSIM, and it can be incorporated with any deep learning neural networks implemented by TensorFlow. Using several representative convolutional neural networks as case studies, we show that DL-RSIM can guide chip designers to choose a reliability-friendly design option and energy-efficient deployment strategies and develop optimization techniques accordingly.

Original languageEnglish
Article number24
JournalACM Transactions on Embedded Computing Systems
Volume21
Issue number3
DOIs
StatePublished - May 2022

Keywords

  • deep learning accelerator
  • energy efficiency
  • processing-in-memory
  • reliability
  • resistive random access memory
  • Simulation framework

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