AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks
文献类型：期刊论文
| 作者 | Gong, Cheng1; Lu, Ye2,3; Dai, Su-Rong2; Deng, Qian2; Du, Cheng-Kun2; Li, Tao2,3 |
| 刊名 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
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| 出版日期 | 2024-03-01 |
| 卷号 | 39期号:2页码:401-420 |
| 关键词 | automatic quantization mixed precision quantizing scheme search quantizing precision learning quantized architecture generation |
| ISSN号 | 1000-9000 |
| DOI | 10.1007/s11390-022-1632-9 |
| 英文摘要 | Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for domain experts, and an automatic compression method is needed. However, the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios. In this paper, we propose an end-to-end framework named AutoQNN, for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor. AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques: quantizing scheme search (QSS), quantizing precision learning (QPL), and quantized architecture generation (QAG). QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the Differentiable Neural Architecture Search (DNAS) algorithm to seek the layer- or model-desired scheme from the set. QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes, to the best of our knowledge. QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint. QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention, to facilitate end-to-end neural network quantization. We have implemented AutoQNN and integrated it into Keras. Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization. For 2-bit weight and activation of AlexNet and ResNet18, AutoQNN can achieve the accuracy results of 59.75% and 68.86%, respectively, and obtain accuracy improvements by up to 1.65% and 1.74%, respectively, compared with state-of-the-art methods. Especially, compared with the full-precision AlexNet and ResNet18, the 2-bit models only slightly incur accuracy degradation by 0.26% and 0.76%, respectively, which can fulfill practical application demands. |
| 资助项目 | China Postdoctoral Science Foundation[2022M721707] ; National Natural Science Foundation of China[62002175] ; National Natural Science Foundation of China[62272248] ; Special Funding for Excellent Enterprise Technology Correspondent of Tianjin[21YDTPJC00380] ; Open Project Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China[ISECCA-202102] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001244495800005 |
| 出版者 | SPRINGER SINGAPORE PTE LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39916]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Tao |
| 作者单位 | 1.Nankai Univ, Coll Software, Tianjin 300350, Peoples R China 2.Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gong, Cheng,Lu, Ye,Dai, Su-Rong,et al. AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2024,39(2):401-420. |
| APA | Gong, Cheng,Lu, Ye,Dai, Su-Rong,Deng, Qian,Du, Cheng-Kun,&Li, Tao.(2024).AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,39(2),401-420. |
| MLA | Gong, Cheng,et al."AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39.2(2024):401-420. |
入库方式： OAI收割
来源：计算技术研究所
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