{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:22:51Z","timestamp":1750220571302,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,22]]},"DOI":"10.1145\/3453688.3461514","type":"proceedings-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T23:13:45Z","timestamp":1624058025000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["MT-DLA"],"prefix":"10.1145","author":[{"given":"Mengdi","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"Capital Normal University, Beijing, China"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & State Key Laboratory of Computer Architecture, Beijing, China"}]},{"given":"Cheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & State Key Laboratory of Computer Architecture, Beijing, China"}]},{"given":"Xiaohan","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xiandong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"e_1_3_2_3_1_1","volume-title":"Real-Time Object Detection,\" in CVPR","author":"Redmon J.","year":"2016","unstructured":"J. Redmon et al., \"You Only Look Once: Unified, Real-Time Object Detection,\" in CVPR, 2016."},{"key":"e_1_3_2_3_2_1","volume-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,\" IEEE Transactions on Pattern Analysis and Machine Intelligence","author":"Badrinarayanan V.","year":"2017","unstructured":"V. Badrinarayanan et al., \"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,\" IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017."},{"key":"e_1_3_2_3_3_1","volume-title":"Learning to Track at 100 FPS with Deep Regression Networks,\" in ECCV","author":"Held D.","year":"2016","unstructured":"D. Held et al., \"Learning to Track at 100 FPS with Deep Regression Networks,\" in ECCV, 2016."},{"key":"e_1_3_2_3_4_1","volume-title":"Mask R-CNN,\" arxiv","author":"He K.","year":"2017","unstructured":"K. He, et al., \"Mask R-CNN,\" arxiv, 2017."},{"key":"e_1_3_2_3_5_1","volume-title":"Dynamic Multi-Task Learning with Convolutional Neural Network,\" in IJCAI","author":"Fang Y.","year":"2017","unstructured":"Y. Fang, et al., \"Dynamic Multi-Task Learning with Convolutional Neural Network,\" in IJCAI, 2017."},{"key":"e_1_3_2_3_6_1","unstructured":"Y. Gao et al. \"NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction \" in CVPR."},{"key":"e_1_3_2_3_7_1","volume-title":"Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights,\" in ECCV","author":"Mallya A.","year":"2018","unstructured":"A. Mallya et al., \"Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights,\" in ECCV, 2018."},{"key":"e_1_3_2_3_8_1","volume-title":"Real-time Joint Semantic Reasoning for Autonomous Driving,\" in IV","author":"Teichmann M.","year":"2018","unstructured":"M. Teichmann, \"MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving,\" in IV, 2018."},{"key":"e_1_3_2_3_9_1","volume-title":"Multi-Task Zipping via Layer-wise Neuron Sharing,\" in NIPS","author":"He X.","year":"2018","unstructured":"X. He et al., \"Multi-Task Zipping via Layer-wise Neuron Sharing,\" in NIPS, 2018."},{"key":"e_1_3_2_3_10_1","volume-title":"Cambricon-S: Addressing Irregularity in Sparse Neural Networks through A Cooperative Software\/Hardware Approach,\" in MICRO","author":"Zhou X.","year":"2018","unstructured":"X. Zhou et al., \"Cambricon-S: Addressing Irregularity in Sparse Neural Networks through A Cooperative Software\/Hardware Approach,\" in MICRO, 2018."},{"key":"e_1_3_2_3_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"e_1_3_2_3_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317753"},{"key":"e_1_3_2_3_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317919"},{"key":"e_1_3_2_3_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001138"},{"key":"e_1_3_2_3_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080254"},{"key":"e_1_3_2_3_16_1","volume-title":"Bit-Pragmatic Deep Neural Network Computing,\" in MICRO","author":"Albericio J.","year":"2017","unstructured":"J. Albericio et al., \"Bit-Pragmatic Deep Neural Network Computing,\" in MICRO, 2017."},{"key":"e_1_3_2_3_17_1","volume-title":"A None-Sparse Inference Accelerator that Distills and Reuses the Computation Redundancy in CNNs,\" in DAC","author":"Wang Y.","year":"2019","unstructured":"Y. Wang et al., \"A None-Sparse Inference Accelerator that Distills and Reuses the Computation Redundancy in CNNs,\" in DAC, 2019."},{"key":"e_1_3_2_3_18_1","volume-title":"UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition,\" in ISCA","author":"Hegde K.","year":"2018","unstructured":"K. Hegde et al., \"UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition,\" in ISCA, 2018."},{"key":"e_1_3_2_3_19_1","volume-title":"RAPIDNN: In-Memory Deep Neural Network Acceleration Framework,\" arXiv","author":"Imani M.","year":"2018","unstructured":"M. Imani et al., \"RAPIDNN: In-Memory Deep Neural Network Acceleration Framework,\" arXiv, 2018."},{"key":"e_1_3_2_3_20_1","volume-title":"LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks,\" in ECCV","author":"Zhang D.","year":"2018","unstructured":"D. Zhang et al., \"LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks,\" in ECCV, 2018."},{"key":"e_1_3_2_3_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001177"},{"volume-title":"A multineural network acceleration architecture.\" in ISCA","year":"2020","key":"e_1_3_2_3_22_1","unstructured":"Eunjin Baek et al., \"A multineural network acceleration architecture.\" in ISCA, 2020."},{"key":"e_1_3_2_3_23_1","volume-title":"Fast and scalable in-memory deep multitask learning via neural weight virtualization,\" in MobiSys","author":"Lee S.","year":"2020","unstructured":"S. Lee et al., \"Fast and scalable in-memory deep multitask learning via neural weight virtualization,\" in MobiSys, 2020."}],"event":{"name":"GLSVLSI '21: Great Lakes Symposium on VLSI 2021","sponsor":["SIGDA ACM Special Interest Group on Design Automation"],"location":"Virtual Event USA","acronym":"GLSVLSI '21"},"container-title":["Proceedings of the 2021 Great Lakes Symposium on VLSI"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3453688.3461514","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3453688.3461514","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:46Z","timestamp":1750195726000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3453688.3461514"}},"subtitle":["An Efficient Multi-Task Deep Learning Accelerator Design"],"short-title":[],"issued":{"date-parts":[[2021,6,22]]},"references-count":23,"alternative-id":["10.1145\/3453688.3461514","10.1145\/3453688"],"URL":"https:\/\/doi.org\/10.1145\/3453688.3461514","relation":{},"subject":[],"published":{"date-parts":[[2021,6,22]]},"assertion":[{"value":"2021-06-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}