Sparsity-Aware Deep Learning Accelerator Design Supporting CNN and LSTM Operations

被引：0

作者：

Hsiao, Shen-Fu ^{[1
]}

Chang, Hsuan-Jui ^{[1
]}

机构：

[1] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung, Taiwan

来源：

2020 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | 2020年

关键词：

sparsity; convolutional neural networks; convolutional layers; fully-connected layers; long-short-term memory (LSTM); PROCESSOR;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Sparsity of data and weights appears in many convolution neural networks (CNN) and recurrent neural networks such as long-short term memory (LSTM). In this paper, we design a sparsity-aware deep learning hardware accelerator exploiting both data and weight sparsity in CNN and LSTM models. The proposed hardware accelerator significantly reduces memory accesses and computations, leading to much lower power consumption.

引用

页数：4

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