{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:49:22Z","timestamp":1778255362416,"version":"3.51.4"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R\\&D Program of China","doi-asserted-by":"crossref","award":["2019YFB1703901"],"award-info":[{"award-number":["2019YFB1703901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62032020, 61960206008, 62032017"],"award-info":[{"award-number":["62032020, 61960206008, 62032017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3102020QD1005"],"award-info":[{"award-number":["3102020QD1005"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014219","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"publisher","award":["62025205, 61725205"],"award-info":[{"award-number":["62025205, 61725205"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2021,3,19]]},"abstract":"<jats:p>There are many deep learning (e.g. DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives. To enable robust and private mobile sensing, DNN tends to be deployed locally on the resource-constrained mobile devices via model compression. The current practice either hand-crafted DNN compression techniques, i.e., for optimizing DNN-relative performance (e.g. parameter size), or on-demand DNN compression methods, i.e., for optimizing hardware-dependent metrics (e.g. latency), cannot be locally online because they require offline retraining to ensure accuracy. Also, none of them have correlated their efforts with runtime adaptive compression to consider the dynamic nature of deployment context of mobile applications. To address those challenges, we present AdaSpring, a context-adaptive and self-evolutionary DNN compression framework. It enables the runtime adaptive DNN compression locally online. Specifically, it presents the ensemble training of a retraining-free and self-evolutionary network to integrate multiple alternative DNN compression configurations (i.e., compressed architectures and weights). It then introduces the runtime search strategy to quickly search for the most suitable compression configurations and evolve the corresponding weights. With evaluation on five tasks across three platforms and a real-world case study, experiment outcomes show that AdaSpring obtains up to 3.1x latency reduction, 4.2x energy efficiency improvement in DNNs, compared to hand-crafted compression techniques, while only incurring \u2264 6.2ms runtime-evolution latency.<\/jats:p>","DOI":"10.1145\/3448125","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T18:56:41Z","timestamp":1617130601000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["AdaSpring"],"prefix":"10.1145","volume":"5","author":[{"given":"Sicong","family":"Liu","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, School of Computer Science, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, School of Computer Science, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Ma","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, School of Computer Science, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, School of Computer Science, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junzhao","family":"Du","sequence":"additional","affiliation":[{"name":"Xidian University, School of Computer Science and Technology, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"International Conference on Machine Learning. 550--559","author":"Bender Gabriel","year":"2018","unstructured":"Gabriel Bender , Pieter-Jan Kindermans , Barret Zoph , Vijay Vasudevan , and Quoc Le . 2018 . Understanding and simplifying one-shot architecture search . In International Conference on Machine Learning. 550--559 . Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le. 2018. Understanding and simplifying one-shot architecture search. In International Conference on Machine Learning. 550--559."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2994551.2994564"},{"key":"e_1_2_1_3_1","first-page":"1","article-title":"Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices","volume":"4","author":"Bhattacharya Sourav","year":"2020","unstructured":"Sourav Bhattacharya , Dionysis Manousakas , Alberto Gil CP Ramos , Stylianos I Venieris , Nicholas D Lane , and Cecilia Mascolo . 2020 . Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices . Proceedings of the IMWUT 4 , 2 (2020), 1 -- 24 . Sourav Bhattacharya, Dionysis Manousakas, Alberto Gil CP Ramos, Stylianos I Venieris, Nicholas D Lane, and Cecilia Mascolo. 2020. Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices. Proceedings of the IMWUT 4, 2 (2020), 1--24.","journal-title":"Proceedings of the IMWUT"},{"key":"e_1_2_1_4_1","volume-title":"Efficient architecture search by network transformation. arXiv preprint arXiv:1707.04873","author":"Cai Han","year":"2017","unstructured":"Han Cai , Tianyao Chen , Weinan Zhang , Yong Yu , and Jun Wang . 2017. Efficient architecture search by network transformation. arXiv preprint arXiv:1707.04873 ( 2017 ). Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang. 2017. Efficient architecture search by network transformation. arXiv preprint arXiv:1707.04873 (2017)."},{"key":"e_1_2_1_5_1","volume-title":"Once-for-all: Train one network and specialize it for efficient deployment. arXiv preprint arXiv:1908.09791","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. arXiv preprint arXiv:1908.09791 (2019). Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. 2019. Once-for-all: Train one network and specialize it for efficient deployment. arXiv preprint arXiv:1908.09791 (2019)."},{"key":"e_1_2_1_6_1","volume-title":"Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332","author":"Cai Han","year":"2018","unstructured":"Han Cai , Ligeng Zhu , and Song Han . 2018 . Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018). Han Cai, Ligeng Zhu, and Song Han. 2018. Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018)."},{"key":"e_1_2_1_7_1","unstructured":"Guobin Chen Wongun Choi Xiang Yu Tony Han and Manmohan Chandraker. 2017. Learning efficient object detection models with knowledge distillation. In Advances in Neural Information Processing Systems. 742--751.  Guobin Chen Wongun Choi Xiang Yu Tony Han and Manmohan Chandraker. 2017. Learning efficient object detection models with knowledge distillation. In Advances in Neural Information Processing Systems. 742--751."},{"key":"e_1_2_1_8_1","first-page":"1","article-title":"METIER: A Deep Multi-Task Learning Based Activity and User Recognition Model Using Wearable Sensors","volume":"4","author":"Chen Ling","year":"2020","unstructured":"Ling Chen , Yi Zhang , and Liangying Peng . 2020 . METIER: A Deep Multi-Task Learning Based Activity and User Recognition Model Using Wearable Sensors . Proceedings of IMWUT 4 , 1 (2020), 1 -- 18 . Ling Chen, Yi Zhang, and Liangying Peng. 2020. METIER: A Deep Multi-Task Learning Based Activity and User Recognition Model Using Wearable Sensors. Proceedings of IMWUT 4, 1 (2020), 1--18.","journal-title":"Proceedings of IMWUT"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3204459"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939839"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00138"},{"key":"e_1_2_1_12_1","volume-title":"A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282","author":"Cheng Yu","year":"2017","unstructured":"Yu Cheng , Duo Wang , Pan Zhou , and Tao Zhang . 2017. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 ( 2017 ). Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2017. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 (2017)."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01166"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_1_15_1","volume-title":"Fast neural network adaptation via parameter remapping and architecture search. arXiv preprint arXiv:2001.02525","author":"Fang Jiemin","year":"2020","unstructured":"Jiemin Fang , Yuzhu Sun , Kangjian Peng , Qian Zhang , Yuan Li , Wenyu Liu , and Xinggang Wang . 2020. Fast neural network adaptation via parameter remapping and architecture search. arXiv preprint arXiv:2001.02525 ( 2020 ). Jiemin Fang, Yuzhu Sun, Kangjian Peng, Qian Zhang, Yuan Li, Wenyu Liu, and Xinggang Wang. 2020. Fast neural network adaptation via parameter remapping and architecture search. arXiv preprint arXiv:2001.02525 (2020)."},{"key":"e_1_2_1_16_1","volume-title":"Catastrophic forgetting in connectionist networks. Trends in cognitive sciences 3, 4","author":"French Robert M","year":"1999","unstructured":"Robert M French . 1999. Catastrophic forgetting in connectionist networks. Trends in cognitive sciences 3, 4 ( 1999 ), 128--135. Robert M French. 1999. Catastrophic forgetting in connectionist networks. Trends in cognitive sciences 3, 4 (1999), 128--135."},{"key":"e_1_2_1_17_1","volume-title":"Dynamic channel pruning: Feature boosting and suppression. arXiv preprint arXiv:1810.05331","author":"Gao Xitong","year":"2018","unstructured":"Xitong Gao , Yiren Zhao , \u0141ukasz Dudziak , Robert Mullins , and Cheng-zhong Xu. 2018. Dynamic channel pruning: Feature boosting and suppression. arXiv preprint arXiv:1810.05331 ( 2018 ). Xitong Gao, Yiren Zhao, \u0141ukasz Dudziak, Robert Mullins, and Cheng-zhong Xu. 2018. Dynamic channel pruning: Feature boosting and suppression. arXiv preprint arXiv:1810.05331 (2018)."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00215"},{"key":"e_1_2_1_19_1","unstructured":"Google. 2017. TensorFlow. https:\/\/goo.gl\/j7HAZJ.  Google. 2017. TensorFlow. https:\/\/goo.gl\/j7HAZJ."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"e_1_2_1_24_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_2_1_25_1","volume-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt","author":"Iandola Forrest N","year":"2016","unstructured":"Forrest N Iandola , Song Han , Matthew W Moskewicz , Khalid Ashraf , William J Dally , and Kurt Keutzer . 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt ; 0.5 MB model size. arXiv preprint arXiv:1602.07360 ( 2016 ). Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt; 0.5 MB model size. arXiv preprint arXiv:1602.07360 (2016)."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.3015531"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/VLSID.2019.00056"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1367"},{"key":"e_1_2_1_29_1","unstructured":"Kaggle. 2019. State Farm Distracted Driver Detection. https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection.  Kaggle. 2019. State Farm Distracted Driver Detection. https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_2_1_31_1","unstructured":"Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. https:\/\/www.tensorflow.org\/datasets\/catalog\/cifar100.  Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. https:\/\/www.tensorflow.org\/datasets\/catalog\/cifar100."},{"key":"e_1_2_1_33_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.  Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105."},{"key":"e_1_2_1_34_1","volume-title":"IMU-Tube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition. arXiv preprint arXiv:2006.05675","author":"Kwon Hyeokhyen","year":"2020","unstructured":"Hyeokhyen Kwon , Catherine Tong , Harish Haresamudram , Yan Gao , Gregory D Abowd , Nicholas D Lane , and Thomas Ploetz . 2020. IMU-Tube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition. arXiv preprint arXiv:2006.05675 ( 2020 ). Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao, Gregory D Abowd, Nicholas D Lane, and Thomas Ploetz. 2020. IMU-Tube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition. arXiv preprint arXiv:2006.05675 (2020)."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2016.7460664"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804262"},{"key":"e_1_2_1_37_1","volume-title":"Dabnet: Depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv preprint arXiv:1907.11357","author":"Li Gen","year":"2019","unstructured":"Gen Li , Inyoung Yun , Jonghyun Kim , and Joongkyu Kim . 2019 . Dabnet: Depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv preprint arXiv:1907.11357 (2019). Gen Li, Inyoung Yun, Jonghyun Kim, and Joongkyu Kim. 2019. Dabnet: Depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv preprint arXiv:1907.11357 (2019)."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623612"},{"key":"e_1_2_1_39_1","volume-title":"Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436","author":"Liu Hanxiao","year":"2017","unstructured":"Hanxiao Liu , Karen Simonyan , Oriol Vinyals , Chrisantha Fernando , and Koray Kavukcuoglu . 2017. Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436 ( 2017 ). Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu. 2017. Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436 (2017)."},{"key":"e_1_2_1_40_1","volume-title":"Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055","author":"Liu Hanxiao","year":"2018","unstructured":"Hanxiao Liu , Karen Simonyan , and Yiming Yang . 2018 . Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018). Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)."},{"key":"e_1_2_1_41_1","unstructured":"Sicong Liu Junzhao Du Kaiming Nan Atlas Wang Yingyan Lin etal 2020. AdaDeep: A Usage-Driven Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles. arXiv preprint arXiv:2006.04432 (2020).  Sicong Liu Junzhao Du Kaiming Nan Atlas Wang Yingyan Lin et al. 2020. AdaDeep: A Usage-Driven Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles. arXiv preprint arXiv:2006.04432 (2020)."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3210240.3210337"},{"key":"e_1_2_1_43_1","volume-title":"Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference. Pattern Recognition","author":"Luo Jian-Hao","year":"2020","unstructured":"Jian-Hao Luo and Jianxin Wu . 2020 . Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference. Pattern Recognition (2020), 107461. Jian-Hao Luo and Jianxin Wu. 2020. Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference. Pattern Recognition (2020), 107461."},{"key":"e_1_2_1_44_1","volume-title":"A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. arXiv preprint arXiv:2006.02903","author":"Ren Pengzhen","year":"2020","unstructured":"Pengzhen Ren , Yun Xiao , Xiaojun Chang , Po-Yao Huang , Zhihui Li , Xiaojiang Chen , and Xin Wang . 2020. A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. arXiv preprint arXiv:2006.02903 ( 2020 ). Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. 2020. A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. arXiv preprint arXiv:2006.02903 (2020)."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00190"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3090082"},{"key":"e_1_2_1_48_1","volume-title":"Piyush Rai, and Vinay P Namboodiri.","author":"Singh Pravendra","year":"2019","unstructured":"Pravendra Singh , Vinay Kumar Verma , Piyush Rai, and Vinay P Namboodiri. 2019 . Play and prune: Adaptive filter pruning for deep model compression. arXiv preprint arXiv:1905.04446 (2019). Pravendra Singh, Vinay Kumar Verma, Piyush Rai, and Vinay P Namboodiri. 2019. Play and prune: Adaptive filter pruning for deep model compression. arXiv preprint arXiv:1905.04446 (2019)."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"e_1_2_1_51_1","unstructured":"UCI. 2017. Dataset for Human Activity Recognition. https:\/\/goo.gl\/m5bRo1.  UCI. 2017. Dataset for Human Activity Recognition. https:\/\/goo.gl\/m5bRo1."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"e_1_2_1_53_1","volume-title":"Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions. arXiv preprint arXiv:1806.09228","author":"Wu Junru","year":"2018","unstructured":"Junru Wu , Yue Wang , Zhenyu Wu , Zhangyang Wang , Ashok Veeraraghavan , and Yingyan Lin . 2018. Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions. arXiv preprint arXiv:1806.09228 ( 2018 ). Junru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan, and Yingyan Lin. 2018. Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions. arXiv preprint arXiv:1806.09228 (2018)."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00919"},{"key":"e_1_2_1_55_1","volume-title":"A Progressive Sub-Network Searching Framework for Dynamic Inference. arXiv preprint arXiv:2009.05681","author":"Yang Li","year":"2020","unstructured":"Li Yang , Zhezhi He , Yu Cao , and Deliang Fan . 2020. A Progressive Sub-Network Searching Framework for Dynamic Inference. arXiv preprint arXiv:2009.05681 ( 2020 ). Li Yang, Zhezhi He, Yu Cao, and Deliang Fan. 2020. A Progressive Sub-Network Searching Framework for Dynamic Inference. arXiv preprint arXiv:2009.05681 (2020)."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.643"},{"key":"e_1_2_1_57_1","first-page":"1","article-title":"ProxiTalk: Activate Speech Input by Bringing Smartphone to the Mouth","volume":"3","author":"Yang Zhican","year":"2019","unstructured":"Zhican Yang , Chun Yu , Fengshi Zheng , and Yuanchun Shi . 2019 . ProxiTalk: Activate Speech Input by Bringing Smartphone to the Mouth . Proceedings of IMWUT 3 , 3 (2019), 1 -- 25 . Zhican Yang, Chun Yu, Fengshi Zheng, and Yuanchun Shi. 2019. ProxiTalk: Activate Speech Input by Bringing Smartphone to the Mouth. Proceedings of IMWUT 3, 3 (2019), 1--25.","journal-title":"Proceedings of IMWUT"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131675"},{"key":"e_1_2_1_59_1","volume-title":"AutoSlim: Towards One-Shot Architecture Search for Channel Numbers. arXiv preprint arXiv:1903.11728","author":"Yu Jiahui","year":"2019","unstructured":"Jiahui Yu and Thomas Huang . 2019. AutoSlim: Towards One-Shot Architecture Search for Channel Numbers. arXiv preprint arXiv:1903.11728 ( 2019 ). Jiahui Yu and Thomas Huang. 2019. AutoSlim: Towards One-Shot Architecture Search for Channel Numbers. arXiv preprint arXiv:1903.11728 (2019)."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2858384"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00257"},{"key":"e_1_2_1_62_1","volume-title":"Theory-inspired path-regularized differential network architecture search. arXiv preprint arXiv:2006.16537","author":"Zhou Pan","year":"2020","unstructured":"Pan Zhou , Caiming Xiong , Richard Socher , and Steven CH Hoi . 2020. Theory-inspired path-regularized differential network architecture search. arXiv preprint arXiv:2006.16537 ( 2020 ). Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi. 2020. Theory-inspired path-regularized differential network architecture search. arXiv preprint arXiv:2006.16537 (2020)."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.04.018"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448125","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:59Z","timestamp":1750195499000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448125"}},"subtitle":["Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications"],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3,19]]}},"alternative-id":["10.1145\/3448125"],"URL":"https:\/\/doi.org\/10.1145\/3448125","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,19]]},"assertion":[{"value":"2021-03-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}