Noise-Tolerant Hardware-Aware Pruning for Deep Neural Networks

Lu，Shun1,2; Chen，Cheng2; Zhang，Kunlong1; Zheng，Yang3; Hu，Zheng3; Hong，Wenjing1; Li，Guiying1,2; Yao，Xin1,2

10.1007/978-3-031-36625-3_11

2023

0302-9743

1611-3349

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

13969 LNCS

127-138

Existing hardware-aware pruning methods for deep neural networks do not take the uncertain execution environment of low-end hardware into consideration. That makes those methods unreliable, since the hardware environments they used for evaluating the pruned models contain uncertainty and thus the performance values contain noise. To deal with this problem, this paper proposes noise-tolerant hardware-aware pruning, i.e., NT-HP. It uses a population-based idea to iteratively generate pruned models. Each pruned model is sent to realistic low-end hardware for performance evaluations. For the noisy values of performance indicators collected from hardware, a threshold for comparison is set, where only the pruned models with significantly better performances are kept in the next generation. Our experimental results show that with the noise-tolerant technique involved, NT-HP can get better pruned models in the uncertain execution environment of low-end hardware.

Deep neural networks Hardware-aware Noise-tolerant neural network pruning Pruning

第一 ; 通讯

英语

EI

20233614671918

Iterative methods

Ergonomics and Human Factors Engineering:461.4 ; Numerical Methods:921.6

2-s2.0-85169412754

Scopus

1.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
3.TTE Lab,Huawei Technologies Co.,Ltd.,Shenzhen,China

Lu，Shun,Chen，Cheng,Zhang，Kunlong,et al. Noise-Tolerant Hardware-Aware Pruning for Deep Neural Networks[C],2023:127-138.