Lin, Wei TingWei TingLinCheng, Hsiang YunHsiang YunChengCHIA-LIN YANGLin, Meng YaoMeng YaoLinLien, KaiKaiLienHu, Han WenHan WenHuChang, Hung ShengHung ShengChangLi, Hsiang PangHsiang PangLiChang, Meng FanMeng FanChangTsou, Yen TingYen TingTsouNien, Chin FuChin FuNien2023-05-182023-05-182022-05-0115399087https://scholars.lib.ntu.edu.tw/handle/123456789/631136Memristor-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.deep learning accelerator | energy efficiency | processing-in-memory | reliability | resistive random access memory | Simulation framework[SDGs]SDG7journal article10.1145/35076392-s2.0-85134577471WOS:000827414100004https://api.elsevier.com/content/abstract/scopus_id/85134577471