An Energy-Efficient Accelerator for Hybrid Bit-width DNNs

被引:0
|
作者
Liu, Bo [1 ]
Ruan, Xing [1 ]
Xia, Mengwen [1 ]
Gong, Yu [1 ]
Yang, Jinjiang [1 ]
Ge, Wei [1 ]
Yang, Jun [1 ]
机构
[1] Southeast Univ, Natl ASIC Syst Engn Technol Res Ctr, Nanjing 210096, Jiangsu, Peoples R China
基金
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词
DNN; neural network compression; approximate multiplier; FPGA;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposed an energy-efficient accelerator for hybrid bit-width DNNs. To speedup the DNNs and make it energy-efficient, we first propose a hybrid bit-width compression method that can save the memory storage of the state-of-the-art network LeNet and AlexNet by 7x and 8x with negligible accuracy loss. Also we propose an energy-efficient iterative logarithmic multiplier with approximate computing to process the multiply-accumulate operations for DNNs. The computational ability and adaptability of the FPGA greatly expands by using the LUT-based multiplier with improved iterative logarithmic rather than a DSP-based multiplier. The accelerator power is reduced from 24.1 W to 14.3 W. The accelerator proposed performs 1.53 times better in power efficiency and can achieve 33.98 GOP/s/W comparing with state-of-the-art architecture.
引用
收藏
页码:3306 / 3313
页数:8
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