@inproceedings{71c2ecf24894400c989d1460e3ee700a,
title = "Work-in-Progress: Toward Energy-efficient Near STT-MRAM Processing Architecture for Neural Networks",
abstract = "The size of parameters in artificial neural network (NN) applications grows quickly from a handful to the GB-level. The data transmission poses a key challenge for NN, and either neuron is removed or data compression reduces pressure on memory access but cannot successfully decrease data traffic. Therefore, we propose the near spin-transfer-torque magnetic random processing architecture for developing energy-efficient NNs. Our approach provides system architects with a preliminary scheme to obtain real-time transmission that near memory controller directly compresses non-zero elements, and encodes the corresponding index depending on the kernel size. Furthermore, it adjusts the number of multiplication accumulators and avoids unnecessary hardware overheads during computation. The preliminary experimental results demonstrated this design verified with weights that currently achieve up to 3.05x speedup and 29.6\% power compared with the unoptimized one.",
keywords = "Energy-efficient, Near-memory Processing, Neural Network, STT-MRAM",
author = "Yueting Li and Bingluo Zhao and Xinyi Xu and Yundong Zhang and Jun Wang and Weisheng Zhao",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2022 ; Conference date: 07-10-2022 Through 14-10-2022",
year = "2022",
doi = "10.1109/CODES-ISSS55005.2022.00013",
language = "英语",
series = "Proceedings - 2022 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "13--14",
booktitle = "Proceedings - 2022 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2022",
address = "美国",
}