{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:12:51Z","timestamp":1773317571947,"version":"3.50.1"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372344, 62171330, and 62172438"],"award-info":[{"award-number":["62372344, 62171330, and 62172438"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R & D Program of China","doi-asserted-by":"crossref","award":["2023YFB3308701"],"award-info":[{"award-number":["2023YFB3308701"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research and Development Plan of Hubei Province","award":["2023BAB075"],"award-info":[{"award-number":["2023BAB075"]}]},{"name":"International Science and Technology Cooperation Project of Hubei Province","award":["2024EHA042"],"award-info":[{"award-number":["2024EHA042"]}]},{"name":"Wuhan Key RD Program Projects","award":["2024050702030091"],"award-info":[{"award-number":["2024050702030091"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220818103200002"],"award-info":[{"award-number":["JCYJ20220818103200002"]}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["2024A1515011155"],"award-info":[{"award-number":["2024A1515011155"]}]},{"name":"Electronic Commerce Fujian province university application technology engineering center, Quanzhou Vocational and Technical University","award":["DZSW24-01"],"award-info":[{"award-number":["DZSW24-01"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>As a distributed embedded system, vehicular edge computing (VEC) completes various complex Deep neural network (DNN) tasks through network collaboration and communication. However,due to the limited computing power of vehicle processors, vehicles cannot handle increasingly complex DNN tasks. To accurately estimate the execution latency of each layer across different DNN models on heterogeneous devices, we proposed the Extreme Gradient Boosting Tree (XGBoost) algorithm to predict DNN task inference latency. Furthermore, we proposed partitioning and offloading algorithms for both chained DNN tasks and Directed Acyclic Graph (DAG)-type DNN tasks, addressing their unique computational characteristics. For chained DNN tasks, we employ a linear search to determine optimal partitioning points based on predictions from the DNN latency prediction model. For the partitioning and offloading of DAG-type DNN tasks, we construct it as a minimum cut problem under the network flow graph and propose a DNN task partitioning and offloading algorithm based on the highest label pre-stream push (HLPP) algorithm to effectively reduce the cost of task partitioning and offloading. Finally, we used an experimental vehicle equipped with Raspberry and a RSU equipped with Jetson Nano to verify the results. The experiment shows that the DNN latency prediction model based on the XGBoost we proposed can effectively improve the latency prediction accuracy of DNN layer-by-layer execution. At the same time, the division and offloading algorithms for different types of DNN inference tasks can achieve higher task completion rate, lower latency, and lower energy consumption.<\/jats:p>","DOI":"10.1145\/3725734","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T11:11:47Z","timestamp":1742555507000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["DNN Inference Acceleration Based on Adaptive Task Partitioning and Offloading in Embedded VEC"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8338-6065","authenticated-orcid":false,"given":"Chunlin","family":"Li","sequence":"first","affiliation":[{"name":"Quanzhou Vocational and Technical University, Quanzhou, China"},{"name":"Wuhan University of Technology, Wuhan China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9194-0150","authenticated-orcid":false,"given":"Sen","family":"Liu","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6304-4637","authenticated-orcid":false,"given":"Kun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7836-3678","authenticated-orcid":false,"given":"Mengjie","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6171-4637","authenticated-orcid":false,"given":"Zihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3631-5720","authenticated-orcid":false,"given":"Bingxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-6850","authenticated-orcid":false,"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University, Shenyang China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4971-5029","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Shenzhen China"}]}],"member":"320","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"R. Botros K. Behrendt L. Novak. 2017. Bosch Small Traffic Lights Dataset. Retrieved from https:\/\/github.com\/bosch-ros-pkg\/bstld"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITNEC56291.2023.10082208"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3233026"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2917440"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3075464"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3259394"},{"key":"e_1_3_1_8_2","first-page":"1","volume-title":"Conference on Robot Learning","author":"Dosovitskiy Alexey","year":"2017","unstructured":"Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017. CARLA: An open urban driving simulator. In Conference on Robot Learning. PMLR, 1\u201316."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3472456.3472468"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3230430"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3241286"},{"key":"e_1_3_1_12_2","unstructured":"State Farm. 2022. State Farm distracted driver detection. Retrieved from https:\/\/www.kaggle.com\/competitions\/state-farm-distracted-driver-detection\/"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3114193"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10001581"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10044-w"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3024577"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3118016"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3384444"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICOIN56518.2023.10048991"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737614"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3093337.3037698"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3155162"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3346506"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3332899"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3441629"},{"issue":"3","key":"e_1_3_1_27_2","first-page":"54:1\u201354:29","article-title":"Deep reinforcement learning-based mining task offloading scheme for intelligent connected vehicles in UAV-aided MEC","volume":"29","author":"Li Chunlin","year":"2024","unstructured":"Chunlin Li, Kun Jiang, Yong Zhang, Lincheng Jiang, and Shaohua Wan. 2024. Deep reinforcement learning-based mining task offloading scheme for intelligent connected vehicles in UAV-aided MEC. ACM Trans. Des. Autom. Electron. Syst. 29, 3 (2024), 54:1\u201354:29.","journal-title":"ACM Trans. Des. Autom. Electron. Syst."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3020542"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3695882"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3388422"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3701234"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2025.3525735"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2023.3258982"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3280484"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.10.033"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2023.3279512"},{"issue":"2","key":"e_1_3_1_37_2","first-page":"2169","article-title":"Mobility-aware multi-hop task offloading for autonomous driving in vehicular edge computing and networks","volume":"24","author":"Liu Lei","year":"2022","unstructured":"Lei Liu, Ming Zhao, Miao Yu, Mian Ahmad Jan, Dapeng Lan, and Amirhosein Taherkordi. 2022. Mobility-aware multi-hop task offloading for autonomous driving in vehicular edge computing and networks. IEEE Trans. Intell. Transp. Syst. 24, 2 (2022), 2169\u20132182.","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2751966"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3218724"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2021.3064579"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2018.00042"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3247889"},{"key":"e_1_3_1_43_2","article-title":"A fine-grained end-to-end latency optimization framework for wireless collaborative inference","author":"Mu Lei","year":"2023","unstructured":"Lei Mu, Zhonghui Li, Wei Xiao, Ruilin Zhang, Peng Wang, Tao Liu, Geyong Min, and Keqin Li. 2023. A fine-grained end-to-end latency optimization framework for wireless collaborative inference. IEEE Internet Things J. (2023).","journal-title":"IEEE Internet Things J."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3025116"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3391831"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03213-1"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC61514.2024.10592446"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2964765"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3175238"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2022.3179820"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2987994"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC57777.2023.10422172"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3274783.3274840"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3174530"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467882"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397315"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00472"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3222408"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3344645"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3318216.3363312"}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3725734","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3725734","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:04Z","timestamp":1750298224000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3725734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,5]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7,31]]}},"alternative-id":["10.1145\/3725734"],"URL":"https:\/\/doi.org\/10.1145\/3725734","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"value":"1539-9087","type":"print"},{"value":"1558-3465","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,5]]},"assertion":[{"value":"2024-08-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}