@inproceedings{fbc063d5c3e04f36a14823a56301c26e,
title = "Time-step interleaved weight reuse for LSTM neural network computing",
abstract = "In Long Short-Term Memory (LSTM) neural network models, a weight matrix tends to be repeatedly loaded from DRAM if the size of on-chip storage of the processor is not large enough to store the entire matrix. To alleviate heavy overhead of DRAM access for weight loading in LSTM computations, we propose a weight reuse scheme which utilizes the weight sharing characteristics in two adjacent time-step computations. Experimental results show that the proposed weight reuse scheme reduces the energy consumption by 28.4-57.3\% and increases the overall throughput by 110.8\% compared to the conventional schemes.",
keywords = "long short-Term memory, weight reuse",
author = "Naebeom Park and Yulhwa Kim and Daehyun Ahn and Taesu Kim and Kim, \{Jae Joon\}",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 ; Conference date: 10-08-2020 Through 12-08-2020",
year = "2020",
month = aug,
day = "10",
doi = "10.1145/3370748.3406561",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020",
}