{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:05:45Z","timestamp":1743041145036,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031429200"},{"type":"electronic","value":"9783031429217"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-42921-7_23","type":"book-chapter","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T05:02:14Z","timestamp":1694754134000},"page":"338-353","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Light-Weight Vision Transformer Toward Near Memory Computation on\u00a0an\u00a0FPGA"],"prefix":"10.1007","author":[{"given":"Takeshi","family":"Senoo","sequence":"first","affiliation":[]},{"given":"Ryota","family":"Kayanoma","sequence":"additional","affiliation":[]},{"given":"Akira","family":"Jinguji","sequence":"additional","affiliation":[]},{"given":"Hiroki","family":"Nakahara","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"23_CR1","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer Normalization. arXiv preprint: arXiv:1607.06450 (2016)"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: DaDianNao: a machine-learning supercomputer. In: Proceedings of the 47th Annual IEEE\/ACM International Symposium on Microarchitecture, pp. 609\u2013622 (2014)","DOI":"10.1109\/MICRO.2014.58"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y.H., Emer, J., Sze, V.: Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks. In: Proceedings of the 43rd Annual International Symposium on Computer Architecture, pp. 367\u2013379 (2016)","DOI":"10.1145\/3007787.3001177"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Chi, P., et al.: PRIME: a novel processing-in-memory architecture for neural network computation in reram-based main memory. In: Proceedings of the 43rd Annual International Symposium on Computer Architecture, pp. 27\u201339 (2016)","DOI":"10.1145\/3007787.3001140"},{"key":"23_CR5","unstructured":"Courbariaux, M., Bengio, Y., David, J.: BinaryConnect: training deep neural networks with binary weights during propagations. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 3123\u20133131 (2015)"},{"key":"23_CR6","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint: arXiv:2010.11929 (2020)"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Gao, M., Ayers, G., Kozyrakis, C.: TETRIS: scalable and efficient neural network acceleration with 3D memory. In: Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 751\u2013764 (2017)","DOI":"10.1145\/3037697.3037702"},{"key":"23_CR8","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 1135\u20131143 (2015)"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR10","unstructured":"Hsieh, K., et al.: The Cacti 9.0 manual. In: Proceedings of the 23rd International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 671\u2013684 (2018)"},{"key":"23_CR11","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 4107\u20134115 (2016)"},{"key":"23_CR12","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 448\u2013456 (2015)"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Knapheide, J., Stabernack, B., Kuhnke, M.: A high throughput MobileNetV2 FPGA implementation based on a flexible architecture for depthwise separable convolution. In: Proceedings of the International Conference on Field-Programmable Logic and Applications (FPL), pp. 277\u2013283 (2020)","DOI":"10.1109\/FPL50879.2020.00053"},{"key":"23_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"23_CR15","unstructured":"Nair, V., Hinton, G.E.: Hinton, rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, pp. 807\u2013814 (2010)"},{"key":"23_CR16","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035 (2019)"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1137\u20131149 (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Yu, W., et al.: MetaFormer is actually what you need for vision (2022). arXiv:2111.11418","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Wu, D., et al.: A high-performance CNN processor based on FPGA for MobileNets. In: Proceedings of the International Conference on Field Programmable Logic and Applications (FPL), pp. 136\u2013143 (2019)","DOI":"10.1109\/FPL.2019.00030"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Yang, Y., et al.: Synetgy: algorithm-hardware co-design for ConvNet accelerators on embedded FPGAs. In: Proceedings of the International Symposium on Field-Programmable Gate Arrays (FPGA), pp. 23\u201332 (2019)","DOI":"10.1145\/3289602.3293902"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., Cong, J.: Optimizing FPGA-based accelerator design for deep convolutional neural networks. In: Proceedings of the 2015 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 161\u2013170 (2015)","DOI":"10.1145\/2684746.2689060"}],"container-title":["Lecture Notes in Computer Science","Applied Reconfigurable Computing. Architectures, Tools, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42921-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T05:04:35Z","timestamp":1694754275000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42921-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031429200","9783031429217"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42921-7_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Applied Reconfigurable Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cottbus","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"arc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/arc2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}