Noise-Tolerant Hardware-Aware Pruning for Deep Neural Networks
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.
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|Document Type||Conference paper|
|Department||Department of Computer Science and Engineering|
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
|First Author Affilication||Department of Computer Science and Engineering; Research Institute of Trustworthy Autonomous Systems|
|Corresponding Author Affilication||Department of Computer Science and Engineering; Research Institute of Trustworthy Autonomous Systems|
|First Author's First Affilication||Department of Computer Science and Engineering|
Lu，Shun,Chen，Cheng,Zhang，Kunlong,et al. Noise-Tolerant Hardware-Aware Pruning for Deep Neural Networks[C],2023:127-138.
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