{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:42:29Z","timestamp":1771612949009,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"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":[[2024,6,23]]},"DOI":"10.1145\/3649329.3657328","type":"proceedings-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T19:27:22Z","timestamp":1731007642000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["DEFA: Efficient Deformable Attention Acceleration via Pruning-Assisted Grid-Sampling and Multi-Scale Parallel Processing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4511-7436","authenticated-orcid":false,"given":"Yansong","family":"Xu","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6826-2670","authenticated-orcid":false,"given":"Dongxu","family":"Lyu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9212-6904","authenticated-orcid":false,"given":"Zhenyu","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9236-0480","authenticated-orcid":false,"given":"Yuzhou","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8882-2345","authenticated-orcid":false,"given":"Zilong","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6944-2958","authenticated-orcid":false,"given":"Gang","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6051-9486","authenticated-orcid":false,"given":"Zhican","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2939-6534","authenticated-orcid":false,"given":"Haomin","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0486-6421","authenticated-orcid":false,"given":"Guanghui","family":"He","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Xizhou Zhu et al. 2021. Deformable DETR: Deformable Transformers for End-to-End Object Detection. In ICLR."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Jifeng Dai et al. 2017. Deformable Convolutional Networks. In ICCV 764--773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Nicolas Carion et al. 2020. End-to-End Object Detection with Transformers. In ECCV 213--229.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_3_2_1_4_1","unstructured":"Feng Li et al. 2022. DN-DETR: Accelerate DETR Training by Introducing Query Denoising. In CVPR 13619--13627."},{"key":"e_1_3_2_1_5_1","volume-title":"DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. In ICLR.","author":"Hao Zhang","year":"2022","unstructured":"Hao Zhang et al. 2022. DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. In ICLR."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Zhiqi Li et al. 2022. BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers. In ECCV 1--18.","DOI":"10.1007\/978-3-031-20077-9_1"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Yiming Li et al. 2023. VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion. In CVPR 9087--9098.","DOI":"10.1109\/CVPR52729.2023.00877"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Yuanhui Huang et al. 2023. Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction. In CVPR 9223--9232.","DOI":"10.1109\/CVPR52729.2023.00890"},{"key":"e_1_3_2_1_9_1","unstructured":"Shaoqing Ren et al. 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. NeurIPS 28."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Hanrui Wang et al. 2021. SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning. In HPCA 97--110.","DOI":"10.1109\/HPCA51647.2021.00018"},{"key":"e_1_3_2_1_11_1","volume-title":"ELSA: Hardware-software co-design for efficient, lightweight self-attention mechanism in neural networks. In ISCA, 692--705.","author":"Ham Tae Jun","year":"2021","unstructured":"Tae Jun Ham et al. 2021. ELSA: Hardware-software co-design for efficient, lightweight self-attention mechanism in neural networks. In ISCA, 692--705."},{"key":"e_1_3_2_1_12_1","first-page":"227","article-title":"An Energy-Efficient Transformer Processor Exploiting Dynamic Weak Relevances in Global Attention","volume":"58","author":"Yang Wang","year":"2022","unstructured":"Yang Wang et al. 2022. An Energy-Efficient Transformer Processor Exploiting Dynamic Weak Relevances in Global Attention. IEEE JSSC, 58, 1, 227--242.","journal-title":"IEEE JSSC"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Qijing Huang et al. 2021. CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs. In FPGA 206--216.","DOI":"10.1145\/3431920.3439295"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Shan Li et al. 2022. A Computational-Efficient Deformable Convolution Network Accelerator via Hardware and Algorithm Co-Optimization. In SiPS 1--6.","DOI":"10.1109\/SiPS55645.2022.9919242"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Tsung-Yi Lin et al. 2014. Microsoft COCO: Common Objects in Context. In ECCV 740--755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Naveen Muralimanohar Rajeev Balasubramonian and Norman P Jouppi. 2009. CACTI 6.0: A tool to model large caches. HP laboratories 27 28.","DOI":"10.1109\/MM.2008.2"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Soroush Ghodrati et al. 2020. Bit-parallel vector composability for neural acceleration. In DAC 1--6.","DOI":"10.1109\/DAC18072.2020.9218656"}],"event":{"name":"DAC '24: 61st ACM\/IEEE Design Automation Conference","location":"San Francisco CA USA","acronym":"DAC '24","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-CEDA","SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 61st ACM\/IEEE Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3657328","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649329.3657328","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:56Z","timestamp":1750295876000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3657328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":17,"alternative-id":["10.1145\/3649329.3657328","10.1145\/3649329"],"URL":"https:\/\/doi.org\/10.1145\/3649329.3657328","relation":{},"subject":[],"published":{"date-parts":[[2024,6,23]]},"assertion":[{"value":"2024-11-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}