{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:22:51Z","timestamp":1775067771984,"version":"3.50.1"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2019YFB2204200"],"award-info":[{"award-number":["2019YFB2204200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4202063"],"award-info":[{"award-number":["4202063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"BJTU-Kuaishou Research Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3004198","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T17:51:21Z","timestamp":1592848281000},"page":"116569-116585","source":"Crossref","is-referenced-by-count":94,"title":["Sparse-YOLO: Hardware\/Software Co-Design of an FPGA Accelerator for YOLOv2"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5351-0448","authenticated-orcid":false,"given":"Zixiao","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-4257","authenticated-orcid":false,"given":"Ke","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5541-4639","authenticated-orcid":false,"given":"Shuaixiao","family":"Wu","sequence":"additional","affiliation":[{"name":"Heterogeneous Computing Group, Kuaishou Technology, Palo Alto, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5329-5832","authenticated-orcid":false,"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"Heterogeneous Computing Group, Kuaishou Technology, Palo Alto, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8596-5199","authenticated-orcid":false,"given":"Lingzhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-8824","authenticated-orcid":false,"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/233269.233324"},{"key":"ref33","first-page":"1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman codings","author":"han","year":"2016","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref32","year":"2020","journal-title":"ShuangXieIrene\/ssds pytorch"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref30","article-title":"An efficient hardware accelerator for structured sparse convolutional neural networks on FPGAs","volume":"abs 2001 1955","author":"zhu","year":"2020","journal-title":"CoRR"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1498765.1498785"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2019.2905242"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021738"},{"key":"ref34","year":"2020","journal-title":"Intel FPGA SDK for opencl pro edition programming guide"},{"key":"ref10","first-page":"1","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"molchanov","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref11","article-title":"Exploring the regularity of sparse structure in convolutional neural networks","volume":"abs 1705 8922","author":"mao","year":"2017","journal-title":"CoRR"},{"key":"ref12","first-page":"1","article-title":"Soft weight-sharing for neural network compression","author":"ullrich","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2808319"},{"key":"ref14","article-title":"Dorefa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients","volume":"abs 1606 6160","author":"zhou","year":"2016","journal-title":"CoRR"},{"key":"ref15","first-page":"3123","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"Proc Adv Neural Inf Proces Syst"},{"key":"ref16","article-title":"PipeCNN: An OpenCL-based FPGA accelerator for large-scale convolution neuron networks","volume":"abs 1611 2450","author":"wang","year":"2016","journal-title":"CoRR"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3289602.3293904"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3174243.3174266"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3154484"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970364"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317753"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2852335"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref9","first-page":"1135","article-title":"Learning both weights and connections for efficient neural networks","author":"han","year":"2015","journal-title":"Proc 28th Adv Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-11515-8_10"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2019.00058"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC.2014.165"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2018.8342100"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/FPL.2018.00088"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-8108-8_21"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/FPT.2018.00043"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09122495.pdf?arnumber=9122495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T17:31:24Z","timestamp":1757698284000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9122495\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3004198","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}