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Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImageNet classification with deep convolutional neural networks,\u201d Advances in Neural Information Processing Systems (NeurIPS), pp.1097-1105, 2012."},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] Y. LeCun, Y. Bengio, and G. Hinton, \u201cDeep learning,\u201d Nature, vol.521, no.7553, pp.436-444, 2015. 10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] J. Redmon and A. Farhadi, \u201cYOLO9000: Better, faster, stronger,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6517-6525, 2017. 10.1109\/cvpr.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] V. Badrinarayanan, A. Kendall, and R. Cipolla, \u201cSegNet: A deep convolutional encoder-decoder architecture for image segmentation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.12, pp.2481-2495, 2017. 10.1109\/tpami.2016.2644615","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei, and Y. Sheikh, \u201cOpenPose: Realtime multi-person 2D pose estimation using part affinity fields,\u201d arXiv preprint arXiv:1812.08008, 2018.","DOI":"10.1109\/CVPR.2017.143"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, \u201cOptimizing FPGA-based accelerator design for deep convolutional neural networks,\u201d International Symposium on Field-Programmable Gate Arrays (FPGA), pp.161-170, 2015. 10.1145\/2684746.2689060","DOI":"10.1145\/2684746.2689060"},{"key":"7","unstructured":"[7] S. Han, J. Pool, J. Tran, and W. Dally, \u201cLearning both weights and connections for efficient neural network,\u201d Advances in Neural Information Processing Systems (NeurIPS), pp.1135-1143, 2015."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N.E. Jerger, and A. Moshovos, \u201cCnvlutin: Ineffectual-neuron-free deep neural network computing,\u201d ACM SIGARCH Computer Architecture News, vol.44, no.3, pp.1-13, 2016. 10.1145\/3007787.3001138","DOI":"10.1145\/3007787.3001138"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] L. Lu, J. Xie, R. Huang, J. Zhang, W. Lin, and Y. Liang, \u201cAn efficient hardware accelerator for sparse convolutional neural networks on FPGAs,\u201d International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp.17-25, 2019. 10.1109\/fccm.2019.00013","DOI":"10.1109\/FCCM.2019.00013"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] T. Jin and S. Hong, \u201cSplit-CNN: Splitting window-based operations in convolutional neural networks for memory system optimization,\u201d International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp.835-847, 2019. 10.1145\/3297858.3304038","DOI":"10.1145\/3297858.3304038"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] A. Jinguji, S. Sato, and H. Nakahara, \u201cTiny on-chip memory realization of weight sparseness split-CNNs on low-end FPGAs,\u201d International Symposium on Field-Programmable Custom Computing Machines (FCCM), p.229, 2020. 10.1109\/fccm48280.2020.00061","DOI":"10.1109\/FCCM48280.2020.00061"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] H. Nakahara, M. Shimoda, and S. Sato, \u201cA tri-state weight convolutional neural network for an FPGA: Applied to YOLOv2 object detector,\u201d International Conference on Field-Programmable Technology (FPT), pp.298-301, 2018. 10.1109\/fpt.2018.00058","DOI":"10.1109\/FPT.2018.00058"},{"key":"13","unstructured":"[13] S. Han, H. Mao, and W.J. Dally, \u201cDeep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,\u201d arXiv preprint arXiv:1510.00149, 2015."},{"key":"14","unstructured":"[14] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556, 2014."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] D. Nguyen, D. Kim, and J. Lee, \u201cDouble MAC: Doubling the performance of convolutional neural networks on modern FPGAs,\u201d Design, Automation and Test in Europe Conference and Exhibition (DATE), pp.890-893, 2017. 10.23919\/date.2017.7927113","DOI":"10.23919\/DATE.2017.7927113"},{"key":"16","unstructured":"[16] Google, \u201cEdge TPU performance benchmarks,\u201d https:\/\/coral.ai\/docs\/edgetpu\/benchmarks\/, [Online; accessed 8-January-2021]."},{"key":"17","unstructured":"[17] K. Guo, L. Sui, J. Qiu, S. Yao, S. Han, Y. Wang, and H. Yang, \u201cFrom model to FPGA: Software-hardware co-design for efficient neural network acceleration,\u201d IEEE Hot Chips 28 Symposium (HCS), pp.1-27, 2016. 10.1109\/hotchips.2016.7936208"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] A. Podili, C. Zhang, and V. Prasanna, \u201cFast and efficient implementation of convolutional neural networks on FPGA,\u201d 2017 IEEE 28th International Conference on Application-Specific Systems, Architectures and Processors (ASAP), pp.11-18, 2017. 10.1109\/asap.2017.7995253","DOI":"10.1109\/ASAP.2017.7995253"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] S. Yin, S. Tang, X. Lin, P. Ouyang, F. Tu, L. Liu, and S. Wei, \u201cA high throughput acceleration for hybrid neural networks with efficient resource management on FPGA,\u201d IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., vol.38, no.4, pp.678-691, 2018. 10.1109\/tcad.2018.2821561","DOI":"10.1109\/TCAD.2018.2821561"},{"key":"20","unstructured":"[20] M. Zhu and S. Gupta, \u201cTo prune, or not to prune: Exploring the efficacy of pruning for model compression,\u201d arXiv preprint arXiv:1710.01878, 2017."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] E. Elsen, M. Dukhan, T. Gale, and K. Simonyan, \u201cFast sparse ConvNets,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.14617-14626, 2020. 10.1109\/cvpr42600.2020.01464","DOI":"10.1109\/CVPR42600.2020.01464"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/12\/E104.D_2021PAP0011\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T03:49:57Z","timestamp":1638589797000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E104.D\/12\/E104.D_2021PAP0011\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,1]]},"references-count":21,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2021pap0011","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,1]]},"article-number":"2021PAP0011"}}