https://scholars.lib.ntu.edu.tw/handle/123456789/631136
標題: | DL-RSIM: A Reliability and Deployment Strategy Simulation Framework for ReRAM-based CNN Accelerators | 作者: | Lin, Wei Ting Cheng, Hsiang Yun CHIA-LIN YANG Lin, Meng Yao Lien, Kai Hu, Han Wen Chang, Hung Sheng Li, Hsiang Pang Chang, Meng Fan Tsou, Yen Ting Nien, Chin Fu |
關鍵字: | deep learning accelerator | energy efficiency | processing-in-memory | reliability | resistive random access memory | Simulation framework | 公開日期: | 1-五月-2022 | 出版社: | ASSOC COMPUTING MACHINERY | 卷: | 21 | 期: | 3 | 來源出版物: | ACM Transactions on Embedded Computing Systems | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/631136 | ISSN: | 15399087 | DOI: | 10.1145/3507639 |
顯示於: | 資訊工程學系 |
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