{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T02:28:14Z","timestamp":1781836094736,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSFC","award":["61872215"],"award-info":[{"award-number":["61872215"]}]},{"name":"Shenzhen Science and Technology Program","award":["RCYX20200714114523079"],"award-info":[{"award-number":["RCYX20200714114523079"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3548001","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:42:35Z","timestamp":1665416555000},"page":"2899-2908","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach"],"prefix":"10.1145","author":[{"given":"Chen","family":"Tang","sequence":"first","affiliation":[{"name":"SIGS, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Zhai","sequence":"additional","affiliation":[{"name":"SIGS, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Ouyang","sequence":"additional","affiliation":[{"name":"SIGS, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[{"name":"SIGS, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Critical learning periods in deep neural networks. arXiv preprint arXiv:1711.08856","author":"Achille Alessandro","year":"2017","unstructured":"Alessandro Achille , Matteo Rovere , and Stefano Soatto . 2017. Critical learning periods in deep neural networks. arXiv preprint arXiv:1711.08856 ( 2017 ). Alessandro Achille, Matteo Rovere, and Stefano Soatto. 2017. Critical learning periods in deep neural networks. arXiv preprint arXiv:1711.08856 (2017)."},{"key":"e_1_3_2_1_2_1","volume-title":"Large scale distributed neural network training through online distillation. arXiv preprint arXiv:1804.03235","author":"Anil Rohan","year":"2018","unstructured":"Rohan Anil , Gabriel Pereyra , Alexandre Passos , Robert Ormandi , George E Dahl , and Geoffrey E Hinton . 2018. Large scale distributed neural network training through online distillation. arXiv preprint arXiv:1804.03235 ( 2018 ). Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E Dahl, and Geoffrey E Hinton. 2018. Large scale distributed neural network training through online distillation. arXiv preprint arXiv:1804.03235 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"Nice: Noise injection and clamping estimation for neural network quantization. Mathematics","author":"Baskin Chaim","year":"2021","unstructured":"Chaim Baskin , Evgenii Zheltonozhkii , Tal Rozen , Natan Liss , Yoav Chai , Eli Schwartz , Raja Giryes , Alexander M Bronstein , and Avi Mendelson . 2021 . Nice: Noise injection and clamping estimation for neural network quantization. Mathematics (2021). Chaim Baskin, Evgenii Zheltonozhkii, Tal Rozen, Natan Liss, Yoav Chai, Eli Schwartz, Raja Giryes, Alexander M Bronstein, and Avi Mendelson. 2021. Nice: Noise injection and clamping estimation for neural network quantization. Mathematics (2021)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00356"},{"key":"e_1_3_2_1_5_1","volume-title":"Bit-Mixer: Mixed-precision networks with runtime bit-width selection. arXiv preprint arXiv:2103.17267","author":"Bulat Adrian","year":"2021","unstructured":"Adrian Bulat and Georgios Tzimiropoulos . 2021. Bit-Mixer: Mixed-precision networks with runtime bit-width selection. arXiv preprint arXiv:2103.17267 ( 2021 ). Adrian Bulat and Georgios Tzimiropoulos. 2021. Bit-Mixer: Mixed-precision networks with runtime bit-width selection. arXiv preprint arXiv:2103.17267 (2021)."},{"key":"e_1_3_2_1_6_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Cai Han","year":"2019","unstructured":"Han Cai , Chuang Gan , Tianzhe Wang , Zhekai Zhang , and Song Han . 2019 . Once-for-All: Train One Network and Specialize it for Efficient Deployment . In International Conference on Learning Representations (ICLR). Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. 2019. Once-for-All: Train One Network and Specialize it for Efficient Deployment. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00242"},{"key":"e_1_3_2_1_8_1","volume-title":"Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning. Advances in Neural Information Processing Systems","author":"Chen Jianda","year":"2020","unstructured":"Jianda Chen , Shangyu Chen , and Sinno Jialin Pan . 2020. Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning. Advances in Neural Information Processing Systems ( 2020 ). Jianda Chen, Shangyu Chen, and Sinno Jialin Pan. 2020. Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning. Advances in Neural Information Processing Systems (2020)."},{"key":"e_1_3_2_1_9_1","volume-title":"Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan.","author":"Choi Jungwook","year":"2018","unstructured":"Jungwook Choi , Zhuo Wang , Swagath Venkataramani , Pierce I-Jen Chuang , Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. 2018 . Pact : Parameterized clipping activation for quantized neural networks. arXiv preprint arXiv:1805.06085 (2018). Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. 2018. Pact: Parameterized clipping activation for quantized neural networks. arXiv preprint arXiv:1805.06085 (2018)."},{"key":"e_1_3_2_1_10_1","volume-title":"HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. In International Conference on Computer Vision (ICCV).","author":"Dong Zhen","year":"2019","unstructured":"Zhen Dong , Zhewei Yao , Amir Gholami , Michael W. Mahoney , and Kurt Keutzer . 2019 . HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. In International Conference on Computer Vision (ICCV). Zhen Dong, Zhewei Yao, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer. 2019. HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. In International Conference on Computer Vision (ICCV)."},{"key":"e_1_3_2_1_11_1","volume-title":"31st British Machine Vision Conference 2020 (BMVC).","author":"Du Kunyuan","year":"2020","unstructured":"Kunyuan Du , Ya Zhang , and Haibing Guan . 2020 . From Quantized DNNs to Quantizable DNNs . In 31st British Machine Vision Conference 2020 (BMVC). Kunyuan Du, Ya Zhang, and Haibing Guan. 2020. From Quantized DNNs to Quantizable DNNs. In 31st British Machine Vision Conference 2020 (BMVC)."},{"key":"e_1_3_2_1_12_1","volume-title":"8th International Conference on Learning Representations (ICLR).","author":"Esser Steven K.","unstructured":"Steven K. Esser , Jeffrey L. McKinstry , Deepika Bablani , Rathinakumar Appuswamy , and Dharmendra S. Modha . 2020. Learned Step Size quantization . In 8th International Conference on Learning Representations (ICLR). Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S. Modha. 2020. Learned Step Size quantization. In 8th International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/SEC50012.2020.00014"},{"key":"e_1_3_2_1_14_1","volume-title":"The Early Phase of Neural Network Training. In International Conference on Learning Representations (ICLR).","author":"Frankle Jonathan","year":"2019","unstructured":"Jonathan Frankle , David J Schwab , and Ari S Morcos . 2019 . The Early Phase of Neural Network Training. In International Conference on Learning Representations (ICLR). Jonathan Frankle, David J Schwab, and Ari S Morcos. 2019. The Early Phase of Neural Network Training. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.113"},{"key":"e_1_3_2_1_16_1","volume-title":"Switchable precision neural networks. arXiv preprint arXiv:2002.02815","author":"Guerra Luis","year":"2020","unstructured":"Luis Guerra , Bohan Zhuang , Ian Reid , and Tom Drummond . 2020. Switchable precision neural networks. arXiv preprint arXiv:2002.02815 ( 2020 ). Luis Guerra, Bohan Zhuang, Ian Reid, and Tom Drummond. 2020. Switchable precision neural networks. arXiv preprint arXiv:2002.02815 (2020)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"e_1_3_2_1_18_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149","author":"Han Song","year":"2015","unstructured":"Song Han , Huizi Mao , and William J Dally . 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 ( 2015 ). Song Han, Huizi Mao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_20_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton , Oriol Vinyals , and Jeff Dean . 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 ( 2015 ). Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_21_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . 2017 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_1_22_1","volume-title":"Multi-Scale Dense Networks for Resource Efficient Image Classification. In International Conference on Learning Representations (ICLR).","author":"Huang Gao","unstructured":"Gao Huang , Danlu Chen , Tianhong Li , Felix Wu , Laurens van der Maaten, and Kilian Weinberger. 2018 . Multi-Scale Dense Networks for Resource Efficient Image Classification. In International Conference on Learning Representations (ICLR). Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Weinberger. 2018. Multi-Scale Dense Networks for Resource Efficient Image Classification. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_23_1","unstructured":"Intel Corp. 2019. Intel Deep Learning Boost. https:\/\/software.intel.com\/content\/www\/us\/en\/develop\/topics\/ai\/deep-learning-boost.html. Accessed: 2021-07-08.  Intel Corp. 2019. Intel Deep Learning Boost. https:\/\/software.intel.com\/content\/www\/us\/en\/develop\/topics\/ai\/deep-learning-boost.html. Accessed: 2021-07-08."},{"key":"e_1_3_2_1_24_1","volume-title":"International conference on machine learning (ICML).","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch normalization: Accelerating deep network training by reducing internal covariate shift . In International conference on machine learning (ICML). Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (ICML)."},{"key":"e_1_3_2_1_25_1","volume-title":"AdaBits: Neural Network Quantization With Adaptive Bit-Widths. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Jin Qing","year":"2020","unstructured":"Qing Jin , Linjie Yang , and Zhenyu Liao . 2020 . AdaBits: Neural Network Quantization With Adaptive Bit-Widths. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Qing Jin, Linjie Yang, and Zhenyu Liao. 2020. AdaBits: Neural Network Quantization With Adaptive Bit-Widths. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_26_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Kaya Yigitcan","year":"2019","unstructured":"Yigitcan Kaya , Sanghyun Hong , and Tudor Dumitras . 2019 . Shallow-deep networks: Understanding and mitigating network overthinking . In International Conference on Machine Learning (ICML). Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitras. 2019. Shallow-deep networks: Understanding and mitigating network overthinking. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475370"},{"key":"e_1_3_2_1_28_1","volume-title":"Ternary weight networks. arXiv preprint arXiv:1605.04711","author":"Li Fengfu","year":"2016","unstructured":"Fengfu Li , Bo Zhang , and Bin Liu . 2016. Ternary weight networks. arXiv preprint arXiv:1605.04711 ( 2016 ). Fengfu Li, Bo Zhang, and Bin Liu. 2016. Ternary weight networks. arXiv preprint arXiv:1605.04711 (2016)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294979"},{"key":"e_1_3_2_1_30_1","volume-title":"Adaptive multi-teacher multi-level knowledge distillation. Neurocomputing","author":"Liu Yuang","year":"2020","unstructured":"Yuang Liu , Wei Zhang , and Jun Wang . 2020. Adaptive multi-teacher multi-level knowledge distillation. Neurocomputing ( 2020 ). Yuang Liu, Wei Zhang, and Jun Wang. 2020. Adaptive multi-teacher multi-level knowledge distillation. Neurocomputing (2020)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"e_1_3_2_1_32_1","volume-title":"AutoQ: Automated Kernel-Wise Neural Network Quantization. In International Conference on Learning Representations (ICLR).","author":"Lou Qian","year":"2019","unstructured":"Qian Lou , Feng Guo , Minje Kim , Lantao Liu , and Lei Jiang . 2019 . AutoQ: Automated Kernel-Wise Neural Network Quantization. In International Conference on Learning Representations (ICLR). Qian Lou, Feng Guo, Minje Kim, Lantao Liu, and Lei Jiang. 2019. AutoQ: Automated Kernel-Wise Neural Network Quantization. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5963"},{"key":"e_1_3_2_1_34_1","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski etal 2015. Human-level control through deep reinforcement learning. nature (2015).  Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski et al. 2015. Human-level control through deep reinforcement learning. nature (2015)."},{"key":"e_1_3_2_1_35_1","volume-title":"Runtime network routing for efficient image classification","author":"Rao Yongming","year":"2018","unstructured":"Yongming Rao , Jiwen Lu , Ji Lin , and Jie Zhou . 2018. Runtime network routing for efficient image classification . IEEE transactions on pattern analysis and machine intelligence ( 2018 ). Yongming Rao, Jiwen Lu, Ji Lin, and Jie Zhou. 2018. Runtime network routing for efficient image classification. IEEE transactions on pattern analysis and machine intelligence (2018)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein etal 2015. Imagenet large scale visual recognition challenge. International journal of computer vision (2015).  Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein et al. 2015. Imagenet large scale visual recognition challenge. International journal of computer vision (2015).","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6025"},{"key":"e_1_3_2_1_39_1","volume-title":"Curl: Contrastive unsupervised representations for reinforcement learning. arXiv preprint arXiv:2004.04136","author":"Srinivas Aravind","year":"2020","unstructured":"Aravind Srinivas , Michael Laskin , and Pieter Abbeel . 2020 . Curl: Contrastive unsupervised representations for reinforcement learning. arXiv preprint arXiv:2004.04136 (2020). Aravind Srinivas, Michael Laskin, and Pieter Abbeel. 2020. Curl: Contrastive unsupervised representations for reinforcement learning. arXiv preprint arXiv:2004.04136 (2020)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_1"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475227"},{"key":"e_1_3_2_1_45_1","volume-title":"HAQ: Hardware-Aware Automated Quantization With Mixed Precision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Wang Kuan","year":"2019","unstructured":"Kuan Wang , Zhijian Liu , Yujun Lin , Ji Lin , and Song Han . 2019 . HAQ: Hardware-Aware Automated Quantization With Mixed Precision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han. 2019. HAQ: Hardware-Aware Automated Quantization With Mixed Precision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_25"},{"key":"e_1_3_2_1_47_1","volume-title":"Mixed precision quantization of convnets via differentiable neural architecture search. arXiv preprint arXiv:1812.00090","author":"Wu Bichen","year":"2018","unstructured":"Bichen Wu , Yanghan Wang , Peizhao Zhang , Yuandong Tian , Peter Vajda , and Kurt Keutzer . 2018b. Mixed precision quantization of convnets via differentiable neural architecture search. arXiv preprint arXiv:1812.00090 ( 2018 ). Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, and Kurt Keutzer. 2018b. Mixed precision quantization of convnets via differentiable neural architecture search. arXiv preprint arXiv:1812.00090 (2018)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00919"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475680"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098135"},{"key":"e_1_3_2_1_51_1","unstructured":"Haichao Yu Haoxiang Li Honghui Shi Thomas S Huang Gang Hua etal 2019. Any-precision deep neural networks. arXiv preprint arXiv:1911.07346 Vol. 1 (2019).  Haichao Yu Haoxiang Li Honghui Shi Thomas S Huang Gang Hua et al. 2019. Any-precision deep neural networks. arXiv preprint arXiv:1911.07346 Vol. 1 (2019)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00189"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_41"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351089"},{"key":"e_1_3_2_1_56_1","volume-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160","author":"Zhou Shuchang","year":"2016","unstructured":"Shuchang Zhou , Yuxin Wu , Zekun Ni , Xinyu Zhou , He Wen , and Yuheng Zou . 2016 . Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016). Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. 2016. Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11623"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2018.8573476"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548001","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548001","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:29Z","timestamp":1750186949000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":58,"alternative-id":["10.1145\/3503161.3548001","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548001","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}