电讯技术2026，Vol.66Issue(1)：71-79,9.DOI:10.20079/j.issn.1001-893x.240926004
面向可重构结构的LSTM混合压缩优化方法
LSTM Hybrid Compression Optimization Method for Reconfigurable Structures
摘要
Abstract
The adaptable and parallel nature of reconfigurable structures has made them ideal for compute-intensive applications,including long short-term memory(LSTM)networks.With the increase of parameters and computation burden,however,it brings about higher demand of storage and bandwidth,which severely limits the computational efficiency.To tackle this problem,a hybrid compression optimization method for LSTM-oriented reconfigurable structures is proposed.Based on the sensitivity of the LSTM network to errors during training,the LSTM network is compressed using different compression algorithms and retrained after compression to analyze the recovery of model accuracy and convergence time,and the gating units in the network are classified into error-sensitive and error-insensitive groups.The gating units in the error-sensitive and error-insensitive groups are compressed using the Top-k pruning strategy and the block-cycle matrix transformation strategy,respectively.In conclusion,the LSTM network is implemented on a reconfigurable array processor built based on Virtex UltraScale VU440 field programmable gate array(FPGA)development board.As the results show,the LSTM network has achieved a compression ratio of 38.4,a hardware acceleration ratio of 1.41,an accuracy loss of about 1.7%,and a reduction in hardware resource consumption.关键词
长短期记忆网络/可重构结构/模型压缩Key words
LSTM/reconfigurable structure/model compression分类
信息技术与安全科学引用本文复制引用
吴海,蒋林,李远成,刘朋飞..面向可重构结构的LSTM混合压缩优化方法[J].电讯技术,2026,66(1):71-79,9.基金项目
科技创新2030"新一代人工智能"重大项目(2022ZD0119005) （2022ZD0119005）
