{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T05:55:06Z","timestamp":1716357306597},"reference-count":75,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2019,5,1]]},"DOI":"10.1587\/transinf.2018rcp0005","type":"journal-article","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T22:23:35Z","timestamp":1556663015000},"page":"1020-1028","source":"Crossref","is-referenced-by-count":1,"title":["Power Efficient Object Detector with an Event-Driven Camera for Moving Object Surveillance on an FPGA"],"prefix":"10.1587","volume":"E102.D","author":[{"given":"Masayuki","family":"SHIMODA","sequence":"first","affiliation":[{"name":"Tokyo Institute of Technology"}]},{"given":"Shimpei","family":"SATO","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology"}]},{"given":"Hiroki","family":"NAKAHARA","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, \u201cGoing deeper with convolutions,\u201d 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, June 2015. 10.1109\/cvpr.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2","unstructured":"[2] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d International Conference on Learning Representations, 2015."},{"key":"3","unstructured":"[3] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImagenet classification with deep convolutional neural networks,\u201d Advances in Neural Information Processing Systems 25, pp.1097-1105, 2012."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] R. Girshick, J. Donahue, T. Darrell, and J. Malik, \u201cRich feature hierarchies for accurate object detection and semantic segmentation,\u201d 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.580-587, June 2014. 10.1109\/cvpr.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M.B. Blaschko and C.H. Lampert, \u201cLearning to localize objects with structured output regression,\u201d ECCV &apos;08 Proc. 10th European Conference on Computer Vision: Part I, pp.2-15, 2008. 10.1007\/978-3-540-88682-2_2","DOI":"10.1007\/978-3-540-88682-2_2"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S.E. Reed, C.-Y. Fu, and A.C. Berg, \u201cSsd: Single shot multibox detector,\u201d European conference on computer vision, pp.21-37, 2016. 10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Redmon and A. Farhadi, \u201cYolo9000: Better, faster, stronger,\u201d 2017 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":"8","doi-asserted-by":"publisher","unstructured":"[8] M. Gottardi, N. Massari, and S.A. Jawed, \u201cA 100 \u00b5 w 128 \u00d7 64 pixels contrast-based asynchronous binary vision sensor for sensor networks applications,\u201d IEEE J. Solid-State Circuits, vol.44, no.5, pp.1582-1592, 2009. 10.1109\/jssc.2009.2017000","DOI":"10.1109\/JSSC.2009.2017000"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] M. Shimoda, S. Sato, and H. Nakahara, Power efficient object detec-tor with an event-driven camera on an FPGA, in Proceedings of the 9th International Symposium on Highly-Efficient Accelerators andRecongurable Technologies, HEART 2018, Toronto, ON, p.10:110:6, Canada, June 2018.","DOI":"10.1145\/3241793.3241803"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] M. Shimoda, S. Sato, and H. Nakahara, \u201cAll binarized convolutional neural network and its implementation on an fpga,\u201d 2017 International Conference on Field Programmable Technology (ICFPT), pp.291-294, Dec. 2017. 10.1109\/fpt.2017.8280163","DOI":"10.1109\/FPT.2017.8280163"},{"key":"11","unstructured":"[11] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, \u201cMobilenets: Efficient convolutional neural networks for mobile vision applications,\u201d arXiv preprint arXiv:1704.04861, 2017."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, \u201cTraffic-sign detection and classification in the wild,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2110-2118, 2016. 10.1109\/cvpr.2016.232","DOI":"10.1109\/CVPR.2016.232"},{"key":"13","unstructured":"[13] J. Li and Z. Wang, \u201cReal-time traffic sign recognition based on efficient cnns in the wild,\u201d IEEE Trans. Intell. Transp. Syst., pp.1-10, 2018."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] E. Peng, F. Chen, and X. Song, \u201cTraffic sign detection with convolutional neural networks,\u201d International Conference on Cognitive Systems and Signal Processing, pp.214-224, 2016. 10.1007\/978-981-10-5230-9_24","DOI":"10.1007\/978-981-10-5230-9_24"},{"key":"15","unstructured":"[15] D. Ribeiro, A. Mateus, J.C. Nascimento, and P. Miraldo, \u201cA real-time pedestrian detector using deep learning for human-aware navigation,\u201d arXiv: Robotics, 2016."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] S. Rujikietgumjorn and N. Watcharapinchai, \u201cReal-time hog-based pedestrian detection in thermal images for an embedded system,\u201d 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.1-6, Aug. 2017. 10.1109\/avss.2017.8078561","DOI":"10.1109\/AVSS.2017.8078561"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] X. Liu, W. Liu, H. Ma, and H. Fu, \u201cLarge-scale vehicle re-identification in urban surveillance videos,\u201d 2016 IEEE International Conference on Multimedia and Expo (ICME), pp.1-6, July 2016. 10.1109\/icme.2016.7553002","DOI":"10.1109\/ICME.2016.7553002"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Y. Tang, D. Wu, Z. Jin, W. Zou, and X. Li, \u201cMulti-modal metric learning for vehicle re-identification in traffic surveillance environment,\u201d 2017 IEEE International Conference on Image Processing (ICIP), pp.2254-2258, Sept. 2017. 10.1109\/icip.2017.8296683","DOI":"10.1109\/ICIP.2017.8296683"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] X. Liu, W. Liu, T. Mei, and H. Ma, \u201cProvid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance,\u201d IEEE Trans. Multimedia, vol.20, no.3, pp.645-658, March 2018. 10.1109\/tmm.2017.2751966","DOI":"10.1109\/TMM.2017.2751966"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] R.S. Feris, B. Siddiquie, J. Petterson, Y. Zhai, A. Datta, L.M. Brown, and S. Pankanti, \u201cLarge-scale vehicle detection, indexing, and search in urban surveillance videos,\u201d IEEE Trans. Multimedia, vol.14, no.1, pp.28-42, Feb. 2012. 10.1109\/tmm.2011.2170666","DOI":"10.1109\/TMM.2011.2170666"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] R. Feris, R. Bobbitt, S. Pankanti, and M.-T. Sun, \u201cEfficient 24\/7 object detection in surveillance videos,\u201d 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.1-6, Aug. 2015. 10.1109\/avss.2015.7301791","DOI":"10.1109\/AVSS.2015.7301791"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] M. Fabbri, S. Calderara, and R. Cucchiara, \u201cGenerative adversarial models for people attribute recognition in surveillance,\u201d 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.1-6, Aug. 2017. 10.1109\/avss.2017.8078521","DOI":"10.1109\/AVSS.2017.8078521"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] C. Bahnsen and T.B. Moeslund, \u201cDetecting road user actions in traffic intersections using rgb and thermal video,\u201d 2015 12th IEEE International Conference on Advanced Video and Signal BasedSurveillance (AVSS), pp.1-6, Aug. 2015. 10.1109\/avss.2015.7301733","DOI":"10.1109\/AVSS.2015.7301733"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] K. Lim, W.-D. Jang, and C.-S. Kim, \u201cBackground subtraction using encoder-decoder structured convolutional neural network,\u201d 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.1-6, Aug. 2017. 10.1109\/avss.2017.8078547","DOI":"10.1109\/AVSS.2017.8078547"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] A. Shimada, H. Nagahara, and R. Taniguchi, \u201cChange detection on light field for active video surveillance,\u201d 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.1-6, Aug. 2015. 10.1109\/avss.2015.7301785","DOI":"10.1109\/AVSS.2015.7301785"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] T. Minematsu, A. Shimada, and R. Taniguchi, \u201cAnalytics of deep neural network in change detection,\u201d 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.1-6, Aug. 2017. 10.1109\/avss.2017.8078550","DOI":"10.1109\/AVSS.2017.8078550"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] F. Bousetouane and B. Morris, \u201cFast cnn surveillance pipeline for fine-grained vessel classification and detection in maritime scenarios,\u201d 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.242-248, Aug. 2016. 10.1109\/avss.2016.7738076","DOI":"10.1109\/AVSS.2016.7738076"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] D.K. Prasad, C.K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, \u201cObject detection in a maritime environment: Performance evaluation of background subtraction methods,\u201d IEEE Trans. Intell. Transp. Syst., pp.1-16, 2018. 10.1109\/tits.2018.2836399","DOI":"10.1109\/TITS.2018.2836399"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] K. He, X. Zhang, S. Ren, and J. Sun, \u201cSpatial pyramid pooling in deep convolutional networks for visual recognition,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.37, no.9, pp.1904-1916, 2015. 10.1109\/tpami.2015.2389824","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] S. Ren, K. He, R.B. Girshick, and J. Sun, \u201cFaster r-cnn: Towards real-time object detection with region proposal networks,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.6, pp.1137-1149, 2017. 10.1109\/tpami.2016.2577031","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] J. Redmon, S.K. Divvala, R.B. Girshick, and A. Farhadi, \u201cYou only look once: Unified, real-time object detection,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.779-788, 2016. 10.1109\/cvpr.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"32","unstructured":"[32] Du Shuxin and Wu Tiejun, \u201cSupport vector machines for pattern recognition,\u201d Journal of Zhejiang University (Engineering Science), 37(5): 521-527, 2003."},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] N. Dalal and B. Triggs, \u201cHistograms of oriented gradients for human detection,\u201d 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR&apos;05), vol.1, pp.886-893, June 2005. 10.1109\/cvpr.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] D.G. Lowe, \u201cDistinctive image features from scale-invariant keypoints,\u201d International Journal of Computer Vision, vol.60, no.2, pp.91-110, 2004. 10.1023\/b:visi.0000029664.99615.94","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] P. Felzenszwalb, D. McAllester, and D. Ramanan, \u201cA discriminatively trained, multiscale, deformable part model,\u201d 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, June 2008. 10.1109\/cvpr.2008.4587597","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] P. Doll\u00e1r, Z. Tu, P. Perona, and S.J. Belongie, \u201cIntegral channel features,\u201d Proc. British Machine Vision Conference, BMVC 2009, London, UK, Sept. 7-10, 2009, pp.1-11, 2009. 10.5244\/c.23.91","DOI":"10.5244\/C.23.91"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] P. Doll\u00e1r, R. Appel, S. Belongie, and P. Perona, \u201cFast feature pyramids for object detection,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.36, no.8, pp.1532-1545, Aug. 2014. 10.1109\/tpami.2014.2300479","DOI":"10.1109\/TPAMI.2014.2300479"},{"key":"38","unstructured":"[38] W. Nam, P. Doll\u00e1r, and J.H. Han, \u201cLocal decorrelation for improved pedestrian detection,\u201d Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Dec. 8-13 2014, Montreal, Quebec, Canada, pp.424-432, 2014."},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] R.B. Girshick, \u201cFast r-cnn,\u201d 2015 IEEE International Conference on Computer Vision (ICCV), pp.1440-1448, 2015. 10.1109\/iccv.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] M.-M. Cheng, J. Warrell, W.-Y. Lin, S. Zheng, V. Vineet, and N. Crook, \u201cEfficient salient region detection with soft image abstraction,\u201d 2013 IEEE International Conference on Computer Vision, pp.1529-1536, Dec. 2013. 10.1109\/iccv.2013.193","DOI":"10.1109\/ICCV.2013.193"},{"key":"41","unstructured":"[41] V.H.C. de Melo, S. Le\u00e3o, D. Menotti, and W.R. Schwartz, \u201cAn optimized sliding window approach to pedestrian detection,\u201d 2014 22nd International Conference on Pattern Recognition, pp.4346-4351, Aug. 2014. 10.1109\/icpr.2014.744"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, \u201cXnor-net: Imagenet classification using binary convolutional neural networks,\u201d European conference on computer vision, pp.525-542, 2016. 10.1007\/978-3-319-46493-0_32","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"43","unstructured":"[43] M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y.Bengio, \u201cBinarized neural networks: Training deep neural networks with weights and activations constrained to +1 or-1,\u201d arXiv preprint arXiv:1602.02830, 2016."},{"key":"44","unstructured":"[44] F. Li and B. Liu, \u201cTernary weight networks,\u201d arXiv preprint arXiv:1605.04711, 2016."},{"key":"45","unstructured":"[45] S. Zhou, Z. Ni, X. Zhou, H. Wen, Y. Wu, and Y. Zou, \u201cDorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients,\u201d arXiv preprint arXiv:1606.06160, 2016."},{"key":"46","unstructured":"[46] D.A. Gudovskiy and L. Rigazio, \u201cShiftcnn: Generalized low-precision architecture for inference of convolutional neural networks,\u201d arXiv preprint arXiv:1706.02393, 2017."},{"key":"47","unstructured":"[47] M. Kim and P. Smaragdis, \u201cBitwise neural networks,\u201d arXiv preprint arXiv:1601.06071, 2016."},{"key":"48","unstructured":"[48] W. Sung, S. Shin, and K. Hwang, \u201cResiliency of deep neural networks under quantization,\u201d arXiv preprint arXiv:1511.06488, 2015."},{"key":"49","unstructured":"[49] S. Han, H. Mao, and W.J. Dally, \u201cDeep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,\u201d International Conference on Learning Representations, 2016."},{"key":"50","unstructured":"[50] M. Courbariaux, Y. Bengio, and J.-P. David, \u201cBinaryconnect: training deep neural networks with binary weights during propagations,\u201d Neural Information Processing Systems, pp.3123-3131, 2015."},{"key":"51","unstructured":"[51] P. Gysel, M. Motamedi, and S. Ghiasi, \u201cHardware-oriented approximation of convolutional neural networks,\u201d arXiv preprint arXiv:1604.03168, 2016."},{"key":"52","doi-asserted-by":"crossref","unstructured":"[52] G. Venkatesh, E. Nurvitadhi, and D. Marr, \u201cAccelerating deep convolutional networks using low-precision and sparsity,\u201d 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2861-2865, 2017. 10.1109\/icassp.2017.7952679","DOI":"10.1109\/ICASSP.2017.7952679"},{"key":"53","unstructured":"[53] D. Miyashita, E.H. Lee, and B. Murmann, \u201cConvolutional neural networks using logarithmic data representation,\u201d arXiv preprint arXiv:1603.01025, 2016."},{"key":"54","doi-asserted-by":"crossref","unstructured":"[54] E. Nurvitadhi, D. Sheffield, J. Sim, A.K. Mishra, G. Venkatesh, and D. Marr, \u201cAccelerating binarized neural networks: Comparison of fpga, cpu, gpu, and asic,\u201d 2016 International Conference on Field-Programmable Technology (FPT), pp.77-84, 2016. 10.1109\/fpt.2016.7929192","DOI":"10.1109\/FPT.2016.7929192"},{"key":"55","doi-asserted-by":"crossref","unstructured":"[55] D.J.M. Moss, E. Nurvitadhi, J. Sim, A.K. Mishra, D. Marr, S.Subhaschandra, and P.H.W. Leong, \u201cHigh performance binary neural networks on the xeon+FPGA<sup>TM<\/sup> platform,\u201d 2017 27th International Conference on Field Programmable Logic and Applications (FPL), pp.1-4, 2017. 10.23919\/fpl.2017.8056823","DOI":"10.23919\/FPL.2017.8056823"},{"key":"56","doi-asserted-by":"crossref","unstructured":"[56] L. Jiao, C. Luo, W. Cao, X. Zhou, and L. Wang, \u201cAccelerating low bit-width convolutional neural networks with embedded fpga,\u201d 2017 27th International Conference on Field Programmable Logic and Applications (FPL), pp.1-4, 2017. 10.23919\/fpl.2017.8056820","DOI":"10.23919\/FPL.2017.8056820"},{"key":"57","doi-asserted-by":"crossref","unstructured":"[57] R. Zhao, W. Song, W. Zhang, T. Xing, J.-H. Lin, M.B.Srivastava, R. Gupta, and Z. Zhang, \u201cAccelerating binarized convolutional neural networks with software-programmablefpgas,\u201d Proc. 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp.15-24, 2017. 10.1145\/3020078.3021741","DOI":"10.1145\/3020078.3021741"},{"key":"58","doi-asserted-by":"crossref","unstructured":"[58] J. Qiu, J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, J. Yu, T. Tang, N. Xu, S. Song, Y. Wang, and H. Yang, \u201cGoing deeper with embedded fpga platform for convolutional neural network,\u201d Proc. 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp.26-35, 2016. 10.1145\/2847263.2847265","DOI":"10.1145\/2847263.2847265"},{"key":"59","doi-asserted-by":"crossref","unstructured":"[59] A. Prost-Boucle, A. Bourge, F. P\u00e9trot, H. Alemdar, N. Caldwell, and V. Leroy, \u201cScalable high-performance architecture for convolutional ternary neural networks on fpga,\u201d 2017 27th International Conference on Field Programmable Logic and Applications (FPL), pp.1-7, Sept. 2017. 10.23919\/fpl.2017.8056850","DOI":"10.23919\/FPL.2017.8056850"},{"key":"60","unstructured":"[60] H. Yonekawa and H. Nakahara, \u201cAn on-chip memory batch normalization free binarized convolutional deep neural network on an fpga,\u201d 24th Reconfigurable Architectures Workshop (RAW 2017), 2017."},{"key":"61","doi-asserted-by":"crossref","unstructured":"[61] C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, \u201cOptimizing fpga-based accelerator design for deep convolutional neural networks,\u201d Proc. 2015 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp.161-170, 2015. 10.1145\/2684746.2689060","DOI":"10.1145\/2684746.2689060"},{"key":"62","doi-asserted-by":"crossref","unstructured":"[62] C. Farabet, Y. LeCun, K. Kavukcuoglu, E. Culurciello, B. Martini, P. Akselrod, and S. Talay, \u201cLarge-scale fpga-based convolutional networks,\u201d Scaling up Machine Learning: Parallel and Distributed Approaches, pp.399-419, 2011.","DOI":"10.1017\/CBO9781139042918.020"},{"key":"63","unstructured":"[63] K. Abdelouahab, M. Pelcat, F. Berry, and J. S&apos;erot, \u201cAccelerating cnn inference on fpgas: A survey,\u201d arXiv preprint arXiv:1806.01683, 2018."},{"key":"64","doi-asserted-by":"crossref","unstructured":"[64] Y. Umuroglu, N.J. Fraser, G. Gambardella, M. Blott, P.H.W. Leong, M. Jahre, and K.A. Vissers, \u201cFinn: A framework for fast, scalable binarized neural network inference,\u201d Proc. 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp.65-74, 2017. 10.1145\/3020078.3021744","DOI":"10.1145\/3020078.3021744"},{"key":"65","doi-asserted-by":"publisher","unstructured":"[65] J. Cong, B. Liu, S. Neuendorffer, J. Noguera, K.A. Vissers, and Z. Zhang, \u201cHigh-level synthesis for fpgas: From prototyping to deployment,\u201d IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol.30, no.4, pp.473-491, 2011. 10.1109\/tcad.2011.2110592","DOI":"10.1109\/TCAD.2011.2110592"},{"key":"66","doi-asserted-by":"crossref","unstructured":"[66] V. Kathail, J. Hwang, W. Sun, Y. Chobe, T. Shui, and J. Carrillo, \u201cSdsoc: A higher-level programming environment for zynq soc and ultrascale+ mpsoc,\u201d Proc. 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, p.4, 2016. 10.1145\/2847263.2847284","DOI":"10.1145\/2847263.2847284"},{"key":"67","doi-asserted-by":"crossref","unstructured":"[67] T.S. Czajkowski, U. Aydonat, D. Denisenko, J. Freeman, M.Kinsner, D. Neto, J. Wong, P. Yiannacouras, and D.P. Singh, \u201cFrom opencl to high-performance hardware on fpgas,\u201d 22nd International Conference on Field Programmable Logic and Applications (FPL), pp.531-534, 2012. 10.1109\/fpl.2012.6339272","DOI":"10.1109\/FPL.2012.6339272"},{"key":"68","doi-asserted-by":"crossref","unstructured":"[68] E. Nurvitadhi, G. Venkatesh, J. Sim, D. Marr, R. Huang, J.O.G. Hock, Y.T. Liew, K. Srivatsan, D.J.M. Moss, S. Subhaschandra, and G. Boudoukh, \u201cCan fpgas beat gpus in accelerating next-generation deep neural networks?,\u201d Proc. 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp.5-14, 2017. 10.1145\/3020078.3021740","DOI":"10.1145\/3020078.3021740"},{"key":"69","unstructured":"[69] S. Ioffe and C. Szegedy, \u201cBatch normalization: Accelerating deep network training by reducing internal covariate shift,\u201d International Conference on Machine Learning, pp.448-456, 2015."},{"key":"70","doi-asserted-by":"crossref","unstructured":"[70] H. Nakahara, T. Fujii, and S. Sato, \u201cA fully connected layer elimination for a binarizec convolutional neural network on an fpga,\u201d 2017 27th International Conference on Field Programmable Logic and Applications (FPL), pp.1-4, Sept. 2017. 10.23919\/fpl.2017.8056771","DOI":"10.23919\/FPL.2017.8056771"},{"key":"71","doi-asserted-by":"publisher","unstructured":"[71] B. Bosi, G. Bois, and Y. Savaria, \u201cReconfigurable pipelined 2-d convolvers for fast digital signal processing,\u201d IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol.7, no.3, pp.299-308, Sept. 1999. 10.1109\/92.784091","DOI":"10.1109\/92.784091"},{"key":"72","doi-asserted-by":"crossref","unstructured":"[72] J. Ferryman and A. Shahrokni, \u201cPets2009: Dataset and challenge,\u201d 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp.1-6, Dec. 2009. 10.1109\/pets-winter.2009.5399556","DOI":"10.1109\/PETS-WINTER.2009.5399556"},{"key":"73","unstructured":"[73] \u201cMultiple object tracking benchmark: 2d mot 2015.\u201d"},{"key":"74","doi-asserted-by":"crossref","unstructured":"[74] H. Nakahara, H. Yonekawa, T. Fujii, M. Shimoda, and S. Sato, \u201cA demonstration of the guinness: A gui based neural network synthesizer for an fpga,\u201d 2017 27th International Conference on Field Programmable Logic and Applications (FPL), p.1, Sept. 2017. 10.23919\/fpl.2017.8056765","DOI":"10.23919\/FPL.2017.8056765"},{"key":"75","unstructured":"[75] S. Tokui, K. Oono, S. Hido, and J. Clayton, \u201cChainer: a next-generation open source framework for deep learning,\u201d in Proceedings of Workshop on Machine Learning Systems (LearningSys) in TheTwenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), 2015."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/5\/E102.D_2018RCP0005\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,4]],"date-time":"2019-05-04T03:25:52Z","timestamp":1556940352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/5\/E102.D_2018RCP0005\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,1]]},"references-count":75,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2018rcp0005","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,1]]}}}