{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:12:56Z","timestamp":1775229176598,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Samsung Research Funding & Incubation Center","award":["SRFC-IT2001-03"],"award-info":[{"award-number":["SRFC-IT2001-03"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,27]]},"DOI":"10.1145\/3498361.3538948","type":"proceedings-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T16:21:53Z","timestamp":1655396513000},"page":"235-247","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":80,"title":["Band"],"prefix":"10.1145","author":[{"given":"Joo Seong","family":"Jeong","sequence":"first","affiliation":[{"name":"Seoul National University"}]},{"given":"Jingyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Seoul National University"}]},{"given":"Donghyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Seoul National University"}]},{"given":"Changmin","family":"Jeon","sequence":"additional","affiliation":[{"name":"Seoul National University"}]},{"given":"Changjin","family":"Jeong","sequence":"additional","affiliation":[{"name":"Seoul National University"}]},{"given":"Youngki","family":"Lee","sequence":"additional","affiliation":[{"name":"Seoul National University"}]},{"given":"Byung-Gon","family":"Chun","sequence":"additional","affiliation":[{"name":"Seoul National University"}]}],"member":"320","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2008. Android Interface Definition Language (AIDL). https:\/\/developer.android.com\/guide\/components\/aidl.  2008. Android Interface Definition Language (AIDL). https:\/\/developer.android.com\/guide\/components\/aidl."},{"key":"e_1_3_2_1_2_1","unstructured":"2008. NVIDIA CUDA. https:\/\/developer.nvidia.com\/cuda-toolkit.  2008. NVIDIA CUDA. https:\/\/developer.nvidia.com\/cuda-toolkit."},{"key":"e_1_3_2_1_3_1","unstructured":"2017. Google NNAPI. https:\/\/developer.android.com\/ndk\/guides\/neuralnetworks.  2017. Google NNAPI. https:\/\/developer.android.com\/ndk\/guides\/neuralnetworks."},{"key":"e_1_3_2_1_4_1","unstructured":"2017. Tencent NCNN. https:\/\/github.com\/Tencent\/ncnn.  2017. Tencent NCNN. https:\/\/github.com\/Tencent\/ncnn."},{"key":"e_1_3_2_1_5_1","unstructured":"2018. Google EdgeTPU. https:\/\/cloud.google.com\/edge-tpu.  2018. Google EdgeTPU. https:\/\/cloud.google.com\/edge-tpu."},{"key":"e_1_3_2_1_6_1","unstructured":"2018. Qualcomm Snapdragon 855 Specs. https:\/\/www.notebookcheck.net\/Qualcomm-Snapdragon-855-SoC-Benchmarks-and-Specs.375436.0.html.  2018. Qualcomm Snapdragon 855 Specs. https:\/\/www.notebookcheck.net\/Qualcomm-Snapdragon-855-SoC-Benchmarks-and-Specs.375436.0.html."},{"key":"e_1_3_2_1_7_1","unstructured":"2018. Xiaomi MACE. https:\/\/github.com\/XiaoMi\/mace.  2018. Xiaomi MACE. https:\/\/github.com\/XiaoMi\/mace."},{"key":"e_1_3_2_1_8_1","unstructured":"2019. TensorFlow Lite. https:\/\/www.tensorflow.org\/lite.  2019. TensorFlow Lite. https:\/\/www.tensorflow.org\/lite."},{"key":"e_1_3_2_1_9_1","unstructured":"2019. Using deep neural networks for accurate hand-tracking on Oculus Quest. https:\/\/ai.facebook.com\/blog\/hand-tracking-deep-neural-networks\/.  2019. Using deep neural networks for accurate hand-tracking on Oculus Quest. https:\/\/ai.facebook.com\/blog\/hand-tracking-deep-neural-networks\/."},{"key":"e_1_3_2_1_10_1","unstructured":"2020. Huawei HiAi DDK. https:\/\/developer.huawei.com\/consumer\/en\/hiai.  2020. Huawei HiAi DDK. https:\/\/developer.huawei.com\/consumer\/en\/hiai."},{"key":"e_1_3_2_1_11_1","unstructured":"2020. Qualcomm Hexagon. https:\/\/developer.qualcomm.com\/software\/hexagon-dsp-sdk\/dsp-processor.  2020. Qualcomm Hexagon. https:\/\/developer.qualcomm.com\/software\/hexagon-dsp-sdk\/dsp-processor."},{"key":"e_1_3_2_1_12_1","unstructured":"2021. Monsoon High Voltage Power Monitor. https:\/\/www.msoon.com\/high-voltage-power-monitor.  2021. Monsoon High Voltage Power Monitor. https:\/\/www.msoon.com\/high-voltage-power-monitor."},{"key":"e_1_3_2_1_13_1","volume-title":"Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks. In PETRA.","author":"Blattgerste Jonas","year":"2017","unstructured":"Jonas Blattgerste , Benjamin Strenge , Patrick Renner , Thies Pfeiffer , and Kai Essig . 2017 . Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks. In PETRA. Jonas Blattgerste, Benjamin Strenge, Patrick Renner, Thies Pfeiffer, and Kai Essig. 2017. Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks. In PETRA."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Michael Buch Zahra Azad Ajay Joshi and Vijay Janapa Reddi. 2021. AI Tax in Mobile SoCs: End-to-end Performance Analysis of Machine Learning in Smartphones. In ISPASS.  Michael Buch Zahra Azad Ajay Joshi and Vijay Janapa Reddi. 2021. AI Tax in Mobile SoCs: End-to-end Performance Analysis of Machine Learning in Smartphones. In ISPASS.","DOI":"10.1109\/ISPASS51385.2021.00027"},{"key":"e_1_3_2_1_15_1","volume-title":"Fsrnet: End-to-end learning face super-resolution with facial priors. In CVPR.","author":"Chen Yu","year":"2018","unstructured":"Yu Chen , Ying Tai , Xiaoming Liu , Chunhua Shen , and Jian Yang . 2018 . Fsrnet: End-to-end learning face super-resolution with facial priors. In CVPR. Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, and Jian Yang. 2018. Fsrnet: End-to-end learning face super-resolution with facial priors. In CVPR."},{"key":"e_1_3_2_1_16_1","volume-title":"PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units","author":"Choi Yujeong","year":"2020","unstructured":"Yujeong Choi and Minsoo Rhu . 2020 . PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units . In HPCA. IEEE , 220--233. Yujeong Choi and Minsoo Rhu. 2020. PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units. In HPCA. IEEE, 220--233."},{"key":"e_1_3_2_1_17_1","volume-title":"Masa: Responsive Multi-Dnn Inference on the Edge. In PerCom.","author":"Cox Bart","year":"2021","unstructured":"Bart Cox , Jeroen Galjaard , Amirmasoud Ghiassi , Robert Birke , and Lydia Y Chen . 2021 . Masa: Responsive Multi-Dnn Inference on the Edge. In PerCom. Bart Cox, Jeroen Galjaard, Amirmasoud Ghiassi, Robert Birke, and Lydia Y Chen. 2021. Masa: Responsive Multi-Dnn Inference on the Edge. In PerCom."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Jiankang Deng Jia Guo Evangelos Ververas Irene Kotsia and Stefanos Zafeiriou. 2020. RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild. In CVPR.  Jiankang Deng Jia Guo Evangelos Ververas Irene Kotsia and Stefanos Zafeiriou. 2020. RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"e_1_3_2_1_19_1","unstructured":"Kuntai Du Ahsan Pervaiz Xin Yuan Aakanksha Chowdhery Qizheng Zhang Henry Hoffmann and Junchen Jiang. 2020. Server-Driven Video Streaming for Deep Learning Inference. In SIGCOMM.  Kuntai Du Ahsan Pervaiz Xin Yuan Aakanksha Chowdhery Qizheng Zhang Henry Hoffmann and Junchen Jiang. 2020. Server-Driven Video Streaming for Deep Learning Inference. In SIGCOMM."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"A. Ess B. Leibe K. Schindler and L. van Gool. 2008. A Mobile Vision System for Robust Multi-Person Tracking. In CVPR.  A. Ess B. Leibe K. Schindler and L. van Gool. 2008. A Mobile Vision System for Robust Multi-Person Tracking. In CVPR.","DOI":"10.1109\/CVPR.2008.4587581"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Biyi Fang Xiao Zeng and Mi Zhang. 2018. NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision. In MobiCom.  Biyi Fang Xiao Zeng and Mi Zhang. 2018. NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision. In MobiCom.","DOI":"10.1145\/3241539.3241559"},{"key":"e_1_3_2_1_22_1","volume-title":"LEO: Scheduling Sensor Inference Algorithms across Heterogeneous Mobile Processors and Network Resources. In MobiCom.","author":"Georgiev Petko","year":"2016","unstructured":"Petko Georgiev , Nicholas D. Lane , Kiran K. Rachuri , and Cecilia Mascolo . 2016 . LEO: Scheduling Sensor Inference Algorithms across Heterogeneous Mobile Processors and Network Resources. In MobiCom. Petko Georgiev, Nicholas D. Lane, Kiran K. Rachuri, and Cecilia Mascolo. 2016. LEO: Scheduling Sensor Inference Algorithms across Heterogeneous Mobile Processors and Network Resources. In MobiCom."},{"key":"e_1_3_2_1_23_1","unstructured":"Arpan Gujarati Reza Karimi Safya Alzayat Wei Hao Antoine Kaufmann Ymir Vigfusson and Jonathan Mace. 2020. Serving DNNs like Clockwork: Performance Predictability from the Bottom Up. In OSDI. 443--462.  Arpan Gujarati Reza Karimi Safya Alzayat Wei Hao Antoine Kaufmann Ymir Vigfusson and Jonathan Mace. 2020. Serving DNNs like Clockwork: Performance Predictability from the Bottom Up. In OSDI. 443--462."},{"key":"e_1_3_2_1_24_1","volume-title":"HERTI: A Reinforcement Learning-Augmented System for Efficient Real-Time Inference on Heterogeneous Embedded Systems. In PACT.","author":"Han Myeonggyun","year":"2021","unstructured":"Myeonggyun Han and Woongki Baek . 2021 . HERTI: A Reinforcement Learning-Augmented System for Efficient Real-Time Inference on Heterogeneous Embedded Systems. In PACT. Myeonggyun Han and Woongki Baek. 2021. HERTI: A Reinforcement Learning-Augmented System for Efficient Real-Time Inference on Heterogeneous Embedded Systems. In PACT."},{"key":"e_1_3_2_1_25_1","volume-title":"MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution for Accurate and Efficient Inference. In PACT.","author":"Han Myeonggyun","year":"2019","unstructured":"Myeonggyun Han , Jihoon Hyun , Seongbeom Park , Jinsu Park , and Woongki Baek . 2019 . MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution for Accurate and Efficient Inference. In PACT. Myeonggyun Han, Jihoon Hyun, Seongbeom Park, Jinsu Park, and Woongki Baek. 2019. MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution for Accurate and Efficient Inference. In PACT."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"e_1_3_2_1_27_1","volume-title":"JeongGil Ko, and Youngki Lee.","author":"Huynh Sinh","year":"2019","unstructured":"Sinh Huynh , Rajesh Krishna Balan , JeongGil Ko, and Youngki Lee. 2019 . VitaMon: Measuring Heart Rate Variability Using Smartphone Front Camera. In SenSys . Sinh Huynh, Rajesh Krishna Balan, JeongGil Ko, and Youngki Lee. 2019. VitaMon: Measuring Heart Rate Variability Using Smartphone Front Camera. In SenSys."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Jun-Woo Jang Sehwan Lee Dongyoung Kim Hyunsun Park Ali Shafiee Ardestani Yeongjae Choi Channoh Kim Yoojin Kim Hyeongseok Yu Hamzah Abdel-Aziz Jun-Seok Park Heonsoo Lee Dongwoo Lee Myeong Woo Kim Hanwoong Jung Heewoo Nam Dongguen Lim Seungwon Lee Joon-Ho Song Suknam Kwon Joseph Hassoun SukHwan Lim and Changkyu Choi. 2021. Sparsity-Aware and Re-configurable NPU Architecture for Samsung Flagship Mobile SoC. In ISCA. 15--28.  Jun-Woo Jang Sehwan Lee Dongyoung Kim Hyunsun Park Ali Shafiee Ardestani Yeongjae Choi Channoh Kim Yoojin Kim Hyeongseok Yu Hamzah Abdel-Aziz Jun-Seok Park Heonsoo Lee Dongwoo Lee Myeong Woo Kim Hanwoong Jung Heewoo Nam Dongguen Lim Seungwon Lee Joon-Ho Song Suknam Kwon Joseph Hassoun SukHwan Lim and Changkyu Choi. 2021. Sparsity-Aware and Re-configurable NPU Architecture for Samsung Flagship Mobile SoC. In ISCA. 15--28.","DOI":"10.1109\/ISCA52012.2021.00011"},{"key":"e_1_3_2_1_29_1","volume-title":"MNN: A Universal and Efficient Inference Engine. In MLSys.","author":"Jiang Xiaotang","year":"2020","unstructured":"Xiaotang Jiang , Huan Wang , Yiliu Chen , Ziqi Wu , Lichuan Wang , Bin Zou , Yafeng Yang , Zongyang Cui , Yu Cai , Tianhang Yu , Chengfei Lv , and Zhihua Wu . 2020 . MNN: A Universal and Efficient Inference Engine. In MLSys. Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lv, and Zhihua Wu. 2020. MNN: A Universal and Efficient Inference Engine. In MLSys."},{"key":"e_1_3_2_1_30_1","volume-title":"Trevor Mudge, Jason Mars, and Lingjia Tang.","author":"Kang Yiping","year":"2017","unstructured":"Yiping Kang , Johann Hauswald , Cao Gao , Austin Rovinski , Trevor Mudge, Jason Mars, and Lingjia Tang. 2017 . Neurosurgeon : Collaborative Intelligence Between the Cloud and Mobile Edge. In ASPLOS. Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang. 2017. Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge. In ASPLOS."},{"key":"e_1_3_2_1_31_1","volume-title":"Graphics processing requirements for enabling immersive vr. AMD White Paper","author":"Kanter David","year":"2015","unstructured":"David Kanter . 2015. Graphics processing requirements for enabling immersive vr. AMD White Paper ( 2015 ). David Kanter. 2015. Graphics processing requirements for enabling immersive vr. AMD White Paper (2015)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Aditya Khosla Akhil S Raju Antonio Torralba and Aude Oliva. 2015. Understanding and predicting image memorability at a large scale. In ICCV. 2390--2398.  Aditya Khosla Akhil S Raju Antonio Torralba and Aude Oliva. 2015. Understanding and predicting image memorability at a large scale. In ICCV. 2390--2398.","DOI":"10.1109\/ICCV.2015.275"},{"key":"e_1_3_2_1_33_1","unstructured":"Youngsok Kim Joonsung Kim Dongju Chae Daehyun Kim and Jangwoo Kim. 2019. &mu;Layer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization. In EuroSys.  Youngsok Kim Joonsung Kim Dongju Chae Daehyun Kim and Jangwoo Kim. 2019. &mu;Layer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization. In EuroSys."},{"key":"e_1_3_2_1_34_1","unstructured":"Dario Korolija Timothy Roscoe and Gustavo Alonso. 2020. Do OS abstractions make sense on FPGAs?. In OSDI. 991--1010.  Dario Korolija Timothy Roscoe and Gustavo Alonso. 2020. Do OS abstractions make sense on FPGAs?. In OSDI. 991--1010."},{"key":"e_1_3_2_1_35_1","volume-title":"Lane and Petko Georgiev","author":"Nicholas","year":"2015","unstructured":"Nicholas D. Lane and Petko Georgiev . 2015 . Can Deep Learning Revolutionize Mobile Sensing?. In HotMobile . Nicholas D. Lane and Petko Georgiev. 2015. Can Deep Learning Revolutionize Mobile Sensing?. In HotMobile."},{"key":"e_1_3_2_1_36_1","unstructured":"Nicholas D. Lane Petko Georgiev and Lorena Qendro. 2015. DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning. In UbiComp.  Nicholas D. Lane Petko Georgiev and Lorena Qendro. 2015. DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning. In UbiComp."},{"key":"e_1_3_2_1_37_1","volume-title":"Lane","author":"Laskaridis Stefanos","year":"2020","unstructured":"Stefanos Laskaridis , Stylianos I. Venieris , Mario Almeida , Ilias Leontiadis , and Nicholas D . Lane . 2020 . SPINN : Synergistic Progressive Inference of Neural Networks over Device and Cloud. In MobiCom . Stefanos Laskaridis, Stylianos I. Venieris, Mario Almeida, Ilias Leontiadis, and Nicholas D. Lane. 2020. SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud. In MobiCom."},{"key":"e_1_3_2_1_38_1","volume-title":"Lane","author":"Lee Royson","year":"2019","unstructured":"Royson Lee , Stylianos I. Venieris , Lukasz Dudziak , Sourav Bhattacharya , and Nicholas D . Lane . 2019 . MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors. In MobiCom . Royson Lee, Stylianos I. Venieris, Lukasz Dudziak, Sourav Bhattacharya, and Nicholas D. Lane. 2019. MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors. In MobiCom."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Gil Levi and Tal Hassner. 2015. Age and gender classification using convolutional neural networks. In CVPRW. 34--42.  Gil Levi and Tal Hassner. 2015. Age and gender classification using convolutional neural networks. In CVPRW. 34--42.","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Gil Levi and Tal Hassner. 2015. Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In ICMI. 503--510.  Gil Levi and Tal Hassner. 2015. Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In ICMI. 503--510.","DOI":"10.1145\/2818346.2830587"},{"key":"e_1_3_2_1_41_1","volume-title":"Starfish: Efficient Concurrency Support for Computer Vision Applications. In MobiSys.","author":"LiKamWa Robert","year":"2015","unstructured":"Robert LiKamWa and Lin Zhong . 2015 . Starfish: Efficient Concurrency Support for Computer Vision Applications. In MobiSys. Robert LiKamWa and Lin Zhong. 2015. Starfish: Efficient Concurrency Support for Computer Vision Applications. In MobiSys."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Akhil Mathur Nicholas D. Lane Sourav Bhattacharya Aidan Boran Claudio Forlivesi and Fahim Kawsar. 2017. DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models Using Wearable Commodity Hardware. In MobiSys.  Akhil Mathur Nicholas D. Lane Sourav Bhattacharya Aidan Boran Claudio Forlivesi and Fahim Kawsar. 2017. DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models Using Wearable Commodity Hardware. In MobiSys.","DOI":"10.1145\/3081333.3081359"},{"key":"e_1_3_2_1_43_1","volume-title":"Tae Jun Ham, and Jae W Lee.","author":"Oh Young H","year":"2021","unstructured":"Young H Oh , Seonghak Kim , Yunho Jin , Sam Son , Jonghyun Bae , Jongsung Lee , Yeonhong Park , Dong Uk Kim , Tae Jun Ham, and Jae W Lee. 2021 . Layerweaver : Maximizing Resource Utilization of Neural Processing Units via Layer-Wise Scheduling. In HPCA. 584--597. Young H Oh, Seonghak Kim, Yunho Jin, Sam Son, Jonghyun Bae, Jongsung Lee, Yeonhong Park, Dong Uk Kim, Tae Jun Ham, and Jae W Lee. 2021. Layerweaver: Maximizing Resource Utilization of Neural Processing Units via Layer-Wise Scheduling. In HPCA. 584--597."},{"key":"e_1_3_2_1_44_1","volume":"200","author":"Park S. K.","unstructured":"S. K. Park , M. S. O'Neill , P. S. Vokonas , D. Sparrow , and J. Schwartz. 200 5. Effects of air pollution on heart rate variability: the VA normative aging study. Environ Health Perspect (2005). S. K. Park, M. S. O'Neill, P. S. Vokonas, D. Sparrow, and J. Schwartz. 2005. Effects of air pollution on heart rate variability: the VA normative aging study. Environ Health Perspect (2005).","journal-title":"J. Schwartz."},{"key":"e_1_3_2_1_45_1","volume-title":"AVR: Augmented vehicular reality. In MobiSys. 81--95.","author":"Qiu Hang","year":"2018","unstructured":"Hang Qiu , Fawad Ahmad , Fan Bai , Marco Gruteser , and Ramesh Govindan . 2018 . AVR: Augmented vehicular reality. In MobiSys. 81--95. Hang Qiu, Fawad Ahmad, Fan Bai, Marco Gruteser, and Ramesh Govindan. 2018. AVR: Augmented vehicular reality. In MobiSys. 81--95."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Kiran K. Rachuri Mirco Musolesi Cecilia Mascolo Peter J. Rentfrow Chris Longworth and Andrius Aucinas. 2010. EmotionSense: A Mobile Phones Based Adaptive Platform for Experimental Social Psychology Research. In UbiComp.  Kiran K. Rachuri Mirco Musolesi Cecilia Mascolo Peter J. Rentfrow Chris Longworth and Andrius Aucinas. 2010. EmotionSense: A Mobile Phones Based Adaptive Platform for Experimental Social Psychology Research. In UbiComp.","DOI":"10.1145\/1864349.1864393"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Bhargava Reddy Ye-Hoon Kim Sojung Yun Chanwon Seo and Junik Jang. 2017. Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks. In CVPRW.  Bhargava Reddy Ye-Hoon Kim Sojung Yun Chanwon Seo and Junik Jang. 2017. Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks. In CVPRW.","DOI":"10.1109\/CVPRW.2017.59"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Wonik Seo Sanghoon Cha Yeonjae Kim Jaehyuk Huh and Jongse Park. 2021. SLO-Aware Inference Scheduler for Heterogeneous Processors in Edge Platforms. ACM Trans. Archit. Code Optim. (2021).  Wonik Seo Sanghoon Cha Yeonjae Kim Jaehyuk Huh and Jongse Park. 2021. SLO-Aware Inference Scheduler for Heterogeneous Processors in Edge Platforms. ACM Trans. Archit. Code Optim. (2021).","DOI":"10.1145\/3460352"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Christian Szegedy Sergey Ioffe Vincent Vanhoucke and Alexander A Alemi. 2017. Inception-v4 inception-resnet and the impact of residual connections on learning. In AAAI.  Christian Szegedy Sergey Ioffe Vincent Vanhoucke and Alexander A Alemi. 2017. Inception-v4 inception-resnet and the impact of residual connections on learning. In AAAI.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In CVPR. 2818--2826.  Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In CVPR. 2818--2826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_1_51_1","volume-title":"Khoa Dang Pham, and Dirk Koch","author":"Vaishnav Anuj","year":"2018","unstructured":"Anuj Vaishnav , Khoa Dang Pham, and Dirk Koch . 2018 . A survey on FPGA virtualization. In FPL. 131--1317. Anuj Vaishnav, Khoa Dang Pham, and Dirk Koch. 2018. A survey on FPGA virtualization. In FPL. 131--1317."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Manni Wang Shaohua Ding Ting Cao Yunxin Liu and Fengyuan Xu. 2021. AsyMo: Scalable and Efficient Deep-Learning Inference on Asymmetric Mobile CPUs. In MobiCom.  Manni Wang Shaohua Ding Ting Cao Yunxin Liu and Fengyuan Xu. 2021. AsyMo: Scalable and Efficient Deep-Learning Inference on Asymmetric Mobile CPUs. In MobiCom.","DOI":"10.1145\/3447993.3448625"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Lior Wolf Tal Hassner and Itay Maoz. 2011. Face recognition in unconstrained videos with matched background similarity. In CVPR. 529--534.  Lior Wolf Tal Hassner and Itay Maoz. 2011. Face recognition in unconstrained videos with matched background similarity. In CVPR. 529--534.","DOI":"10.1109\/CVPR.2011.5995566"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS46320.2019.00042"},{"key":"e_1_3_2_1_55_1","unstructured":"Mengwei Xu Zhe Fu Xiao Ma Li Zhang Yanan Li Feng Qian Shangguang Wang Ke Li Jingyu Yang and Xuanzhe Liu. 2021. From Cloud to Edge: A First Look at Public Edge Platforms. In ACM IMC.  Mengwei Xu Zhe Fu Xiao Ma Li Zhang Yanan Li Feng Qian Shangguang Wang Ke Li Jingyu Yang and Xuanzhe Liu. 2021. From Cloud to Edge: A First Look at Public Edge Platforms. In ACM IMC."},{"key":"e_1_3_2_1_56_1","volume-title":"An evaluation of edge tpu accelerators for convolutional neural networks. arXiv preprint arXiv:2102.10423","author":"Yazdanbakhsh Amir","year":"2021","unstructured":"Amir Yazdanbakhsh , Kiran Seshadri , Berkin Akin , James Laudon , and Ravi Narayanaswami . 2021. An evaluation of edge tpu accelerators for convolutional neural networks. arXiv preprint arXiv:2102.10423 ( 2021 ). Amir Yazdanbakhsh, Kiran Seshadri, Berkin Akin, James Laudon, and Ravi Narayanaswami. 2021. An evaluation of edge tpu accelerators for convolutional neural networks. arXiv preprint arXiv:2102.10423 (2021)."},{"key":"e_1_3_2_1_57_1","unstructured":"Juheon Yi Sunghyun Choi and Youngki Lee. 2020. EagleEye: Wearable Camera-Based Person Identification in Crowded Urban Spaces. In MobiCom.  Juheon Yi Sunghyun Choi and Youngki Lee. 2020. EagleEye: Wearable Camera-Based Person Identification in Crowded Urban Spaces. In MobiCom."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419192"},{"key":"e_1_3_2_1_59_1","volume-title":"Jie Yang, Yanmin Zhu, Zhongyang Chen, Guangtao Xue, and Minglu Li.","author":"Yu Jiadi","year":"2016","unstructured":"Jiadi Yu , Hongzi Zhu , Haofu Han , Yingying Jennifer Chen , Jie Yang, Yanmin Zhu, Zhongyang Chen, Guangtao Xue, and Minglu Li. 2016 . SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments. IEEE Transactions on Mobile Computing ( 2016). Jiadi Yu, Hongzi Zhu, Haofu Han, Yingying Jennifer Chen, Jie Yang, Yanmin Zhu, Zhongyang Chen, Guangtao Xue, and Minglu Li. 2016. SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments. IEEE Transactions on Mobile Computing (2016)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"crossref","unstructured":"Xiao Zeng Biyi Fang Haichen Shen and Mi Zhang. 2020. Distream: scaling live video analytics with workload-adaptive distributed edge intelligence. In SenSys. 409--421.  Xiao Zeng Biyi Fang Haichen Shen and Mi Zhang. 2020. Distream: scaling live video analytics with workload-adaptive distributed edge intelligence. In SenSys. 409--421.","DOI":"10.1145\/3384419.3430721"}],"event":{"name":"MobiSys '22: The 20th Annual International Conference on Mobile Systems, Applications and Services","location":"Portland Oregon","acronym":"MobiSys '22","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3498361.3538948","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3498361.3538948","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:04Z","timestamp":1750183804000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3498361.3538948"}},"subtitle":["coordinated multi-DNN inference on heterogeneous mobile processors"],"short-title":[],"issued":{"date-parts":[[2022,6,27]]},"references-count":60,"alternative-id":["10.1145\/3498361.3538948","10.1145\/3498361"],"URL":"https:\/\/doi.org\/10.1145\/3498361.3538948","relation":{},"subject":[],"published":{"date-parts":[[2022,6,27]]},"assertion":[{"value":"2022-06-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}