{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T16:45:52Z","timestamp":1785602752352,"version":"3.56.0"},"publisher-location":"Cham","reference-count":107,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200526","type":"print"},{"value":"9783031200533","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20053-3_3","type":"book-chapter","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:21:52Z","timestamp":1667665312000},"page":"36-56","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Neural Architecture Search for\u00a0Spiking Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3542-7720","authenticated-orcid":false,"given":"Youngeun","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6444-7253","authenticated-orcid":false,"given":"Yuhang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0787-2082","authenticated-orcid":false,"given":"Hyoungseob","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeshwanth","family":"Venkatesha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4167-6782","authenticated-orcid":false,"given":"Priyadarshini","family":"Panda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"3_CR1","unstructured":"Abdelfattah, M.S., Mehrotra, A., Dudziak, \u0141., Lane, N.D.: Zero-cost proxies for lightweight NAS. arXiv preprint arXiv:2101.08134 (2021)"},{"key":"3_CR2","unstructured":"Baker, B., Gupta, O., Naik, N., Raskar, R.: Designing neural network architectures using reinforcement learning. arXiv preprint arXiv:1611.02167 (2016)"},{"issue":"1","key":"3_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-17236-y","volume":"11","author":"G Bellec","year":"2020","unstructured":"Bellec, G.: A solution to the learning dilemma for recurrent networks of spiking neurons. Nat. Commun. 11(1), 1\u201315 (2020)","journal-title":"Nat. Commun."},{"key":"3_CR4","unstructured":"Bender, G., Kindermans, P.J., Zoph, B., Vasudevan, V., Le, Q.: Understanding and simplifying one-shot architecture search. In: International Conference on Machine Learning, pp. 550\u2013559. PMLR (2018)"},{"key":"3_CR5","unstructured":"Brock, A., Lim, T., Ritchie, J.M., Weston, N.: Smash: one-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344 (2017)"},{"key":"3_CR6","unstructured":"Cai, H., Zhu, L., Han, S.: ProxylessNAS: direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018)"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s11263-014-0788-3","volume":"113","author":"Y Cao","year":"2015","unstructured":"Cao, Y., Chen, Y., Khosla, D.: Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vision 113(1), 54\u201366 (2015). https:\/\/doi.org\/10.1007\/s11263-014-0788-3","journal-title":"Int. J. Comput. Vision"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Chen, B., et al.: BN-NAS: neural architecture search with batch normalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 307\u2013316 (2021)","DOI":"10.1109\/ICCV48922.2021.00037"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Chen, B., et al.: GLiT: neural architecture search for global and local image transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12\u201321 (2021)","DOI":"10.1109\/ICCV48922.2021.00008"},{"key":"3_CR10","unstructured":"Chen, W., Gong, X., Wang, Z.: Neural architecture search on ImageNet in four GPU hours: a theoretically inspired perspective. arXiv preprint arXiv:2102.11535 (2021)"},{"key":"3_CR11","first-page":"6642","volume":"32","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., Sun, J.: DetNAS: backbone search for object detection. Adv. Neural. Inf. Process. Syst. 32, 6642\u20136652 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR12","unstructured":"Christensen, D.V., et al.: 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Comput. Eng. 2, 022501 (2022)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Comsa, I.M., Fischbacher, T., Potempa, K., Gesmundo, A., Versari, L., Alakuijala, J.: Temporal coding in spiking neural networks with alpha synaptic function. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8529\u20138533. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9053856"},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3389\/fninf.2018.00079","volume":"12","author":"V Demin","year":"2018","unstructured":"Demin, V., Nekhaev, D.: Recurrent spiking neural network learning based on a competitive maximization of neuronal activity. Front. Neuroinform. 12, 79 (2018)","journal-title":"Front. Neuroinform."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3_CR16","unstructured":"Deng, S., Gu, S.: Optimal conversion of conventional artificial neural networks to spiking neural networks. arXiv preprint arXiv:2103.00476 (2021)"},{"key":"3_CR17","unstructured":"Deng, S., Li, Y., Zhang, S., Gu, S.: Temporal efficient training of spiking neural network via gradient re-weighting. arXiv preprint arXiv:2202.11946 (2022)"},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3389\/fncom.2015.00099","volume":"9","author":"PU Diehl","year":"2015","unstructured":"Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015)","journal-title":"Front. Comput. Neurosci."},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Diehl, P.U., Neil, D., Binas, J., Cook, M., Liu, S.C., Pfeiffer, M.: Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2015)","DOI":"10.1109\/IJCNN.2015.7280696"},{"key":"3_CR20","unstructured":"Dong, X., Yang, Y.: NAS-bench-201: extending the scope of reproducible neural architecture search. arXiv preprint arXiv:2001.00326 (2020)"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Duan, Y., et al.: TransNAS-bench-101: improving transferability and generalizability of cross-task neural architecture search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5251\u20135260 (2021)","DOI":"10.1109\/CVPR46437.2021.00521"},{"key":"3_CR22","unstructured":"Fang, W., et al.: Spikingjelly (2020). https:\/\/github.com\/fangwei123456\/spikingjelly"},{"key":"3_CR23","unstructured":"Fang, W., Yu, Z., Chen, Y., Huang, T., Masquelier, T., Tian, Y.: Deep residual learning in spiking neural networks. arXiv preprint arXiv:2102.04159 (2021)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Fang, W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., Tian, Y.: Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2661\u20132671 (2021)","DOI":"10.1109\/ICCV48922.2021.00266"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Garg, I., Chowdhury, S.S., Roy, K.: DCT-SNN: using DCT to distribute spatial information over time for low-latency spiking neural networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4671\u20134680 (2021)","DOI":"10.1109\/ICCV48922.2021.00463"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Gong, X., Chang, S., Jiang, Y., Wang, Z.: AutoGAN: neural architecture search for generative adversarial networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3224\u20133234 (2019)","DOI":"10.1109\/ICCV.2019.00332"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Gu, P., Xiao, R., Pan, G., Tang, H.: STCA: spatio-temporal credit assignment with delayed feedback in deep spiking neural networks. In: IJCAI, pp. 1366\u20131372 (2019)","DOI":"10.24963\/ijcai.2019\/189"},{"key":"3_CR28","doi-asserted-by":"publisher","unstructured":"Guo, Z., et al.: Single path one-shot neural architecture search with uniform sampling. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 544\u2013560. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_32","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Han, B., Srinivasan, G., Roy, K.: RMP-SNN: residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13558\u201313567 (2020)","DOI":"10.1109\/CVPR42600.2020.01357"},{"key":"3_CR30","unstructured":"Hanin, B., Rolnick, D.: Complexity of linear regions in deep networks. In: International Conference on Machine Learning, pp. 2596\u20132604. PMLR (2019)"},{"key":"3_CR31","unstructured":"Hanin, B., Rolnick, D.: Deep ReLU networks have surprisingly few activation patterns (2019)"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Hu, S., et al.: DSNAS: direct neural architecture search without parameter retraining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12084\u201312092 (2020)","DOI":"10.1109\/CVPR42600.2020.01210"},{"key":"3_CR35","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"issue":"6","key":"3_CR36","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"EM Izhikevich","year":"2003","unstructured":"Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569\u20131572 (2003)","journal-title":"IEEE Trans. Neural Netw."},{"key":"3_CR37","doi-asserted-by":"publisher","first-page":"205","DOI":"10.3389\/fnins.2021.654786","volume":"15","author":"S Jia","year":"2021","unstructured":"Jia, S., Zhang, T., Cheng, X., Liu, H., Xu, B.: Neuronal-plasticity and reward-propagation improved recurrent spiking neural networks. Front. Neurosci. 15, 205 (2021)","journal-title":"Front. Neurosci."},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Jin, X., Rast, A., Galluppi, F., Davies, S., Furber, S.: Implementing spike-timing-dependent plasticity on spinnaker neuromorphic hardware. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2010)","DOI":"10.1109\/IJCNN.2010.5596372"},{"key":"3_CR39","doi-asserted-by":"crossref","unstructured":"Kim, Y., Panda, P.: Revisiting batch normalization for training low-latency deep spiking neural networks from scratch. arXiv preprint arXiv:2010.01729 (2020)","DOI":"10.3389\/fnins.2021.773954"},{"key":"3_CR40","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.neunet.2021.09.022","volume":"144","author":"Y Kim","year":"2021","unstructured":"Kim, Y., Panda, P.: Optimizing deeper spiking neural networks for dynamic vision sensing. Neural Netw. 144, 686\u2013698 (2021)","journal-title":"Neural Netw."},{"key":"3_CR41","doi-asserted-by":"publisher","first-page":"19037","DOI":"10.1038\/s41598-021-98448-0","volume":"11","author":"Y Kim","year":"2021","unstructured":"Kim, Y., Panda, P.: Visual explanations from spiking neural networks using interspike intervals. Sci. Rep. 11, 19037 (2021). https:\/\/doi.org\/10.1038\/s41598-021-98448-0","journal-title":"Sci. Rep."},{"key":"3_CR42","doi-asserted-by":"crossref","unstructured":"Kim, Y., Venkatesha, Y., Panda, P.: PrivateSNN: fully privacy-preserving spiking neural networks. arXiv preprint arXiv:2104.03414 (2021)","DOI":"10.1609\/aaai.v36i1.20005"},{"key":"3_CR43","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"3_CR44","doi-asserted-by":"crossref","unstructured":"Kundu, S., Datta, G., Pedram, M., Beerel, P.A.: Spike-thrift: towards energy-efficient deep spiking neural networks by limiting spiking activity via attention-guided compression. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3953\u20133962 (2021)","DOI":"10.1109\/WACV48630.2021.00400"},{"key":"3_CR45","doi-asserted-by":"crossref","unstructured":"Kundu, S., Pedram, M., Beerel, P.A.: Hire-SNN: harnessing the inherent robustness of energy-efficient deep spiking neural networks by training with crafted input noise. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5209\u20135218 (2021)","DOI":"10.1109\/ICCV48922.2021.00516"},{"key":"3_CR46","unstructured":"Ledinauskas, E., Ruseckas, J., Jur\u0161\u0117nas, A., Bura\u010das, G.: Training deep spiking neural networks. arXiv preprint arXiv:2006.04436 (2020)"},{"key":"3_CR47","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3389\/fnins.2020.00119","volume":"14","author":"C Lee","year":"2020","unstructured":"Lee, C., Sarwar, S.S., Panda, P., Srinivasan, G., Roy, K.: Enabling spike-based backpropagation for training deep neural network architectures. Front. Neurosci. 14, 119 (2020)","journal-title":"Front. Neurosci."},{"key":"3_CR48","doi-asserted-by":"publisher","first-page":"508","DOI":"10.3389\/fnins.2016.00508","volume":"10","author":"JH Lee","year":"2016","unstructured":"Lee, J.H., Delbruck, T., Pfeiffer, M.: Training deep spiking neural networks using backpropagation. Front. Neurosci. 10, 508 (2016)","journal-title":"Front. Neurosci."},{"key":"3_CR49","unstructured":"Li, Y., Deng, S., Dong, X., Gong, R., Gu, S.: A free lunch from ANN: towards efficient, accurate spiking neural networks calibration. arXiv preprint arXiv:2106.06984 (2021)"},{"key":"3_CR50","unstructured":"Li, Y., Deng, S., Dong, X., Gu, S.: Converting artificial neural networks to spiking neural networks via parameter calibration. arXiv preprint arXiv:2205.10121 (2022)"},{"key":"3_CR51","first-page":"23426","volume":"34","author":"Y Li","year":"2021","unstructured":"Li, Y., Guo, Y., Zhang, S., Deng, S., Hai, Y., Gu, S.: Differentiable spike: rethinking gradient-descent for training spiking neural networks. Adv. Neural. Inf. Process. Syst. 34, 23426\u201323439 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR52","doi-asserted-by":"crossref","unstructured":"Liang, L., et al.: H2learn: high-efficiency learning accelerator for high-accuracy spiking neural networks. arXiv preprint arXiv:2107.11746 (2021)","DOI":"10.1109\/TCAD.2021.3138347"},{"key":"3_CR53","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: Auto-DeepLab: hierarchical neural architecture search for semantic image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 82\u201392 (2019)","DOI":"10.1109\/CVPR.2019.00017"},{"key":"3_CR54","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTs: differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)"},{"key":"3_CR55","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"3_CR56","doi-asserted-by":"publisher","first-page":"535","DOI":"10.3389\/fnins.2020.00535","volume":"14","author":"S Lu","year":"2020","unstructured":"Lu, S., Sengupta, A.: Exploring the connection between binary and spiking neural networks. Front. Neurosci. 14, 535 (2020)","journal-title":"Front. Neurosci."},{"key":"3_CR57","unstructured":"Mellor, J., Turner, J., Storkey, A., Crowley, E.J.: Neural architecture search without training. In: International Conference on Machine Learning, pp. 7588\u20137598. PMLR (2021)"},{"key":"3_CR58","unstructured":"Mont\u00fafar, G., Pascanu, R., Cho, K., Bengio, Y.: On the number of linear regions of deep neural networks. arXiv preprint arXiv:1402.1869 (2014)"},{"issue":"7","key":"3_CR59","first-page":"3227","volume":"29","author":"H Mostafa","year":"2017","unstructured":"Mostafa, H.: Supervised learning based on temporal coding in spiking neural networks. IEEE Trans. Neural Net. Learn. Syst. 29(7), 3227\u20133235 (2017)","journal-title":"IEEE Trans. Neural Net. Learn. Syst."},{"key":"3_CR60","unstructured":"Na, B., Mok, J., Park, S., Lee, D., Choe, H., Yoon, S.: AutoSNN: towards energy-efficient spiking neural networks. arXiv preprint arXiv:2201.12738 (2022)"},{"key":"3_CR61","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/MSP.2019.2931595","volume":"36","author":"EO Neftci","year":"2019","unstructured":"Neftci, E.O., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural networks. IEEE Sign. Process. Mag. 36, 61\u201363 (2019)","journal-title":"IEEE Sign. Process. Mag."},{"key":"3_CR62","doi-asserted-by":"publisher","first-page":"653","DOI":"10.3389\/fnins.2020.00653","volume":"14","author":"P Panda","year":"2020","unstructured":"Panda, P., Aketi, S.A., Roy, K.: Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization. Front. Neurosci. 14, 653 (2020)","journal-title":"Front. Neurosci."},{"key":"3_CR63","doi-asserted-by":"publisher","first-page":"693","DOI":"10.3389\/fnins.2017.00693","volume":"11","author":"P Panda","year":"2017","unstructured":"Panda, P., Roy, K.: Learning to generate sequences with combination of Hebbian and non-Hebbian plasticity in recurrent spiking neural networks. Front. Neurosci. 11, 693 (2017)","journal-title":"Front. Neurosci."},{"key":"3_CR64","doi-asserted-by":"crossref","unstructured":"Park, S., Kim, S., Na, B., Yoon, S.: T2fSNN: deep spiking neural networks with time-to-first-spike coding. arXiv preprint arXiv:2003.11741 (2020)","DOI":"10.1109\/DAC18072.2020.9218689"},{"key":"3_CR65","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)"},{"key":"3_CR66","unstructured":"Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: International Conference on Machine Learning, pp. 4095\u20134104. PMLR (2018)"},{"key":"3_CR67","unstructured":"Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., Sohl-Dickstein, J.: On the expressive power of deep neural networks. In: International Conference on Machine Learning, pp. 2847\u20132854. PMLR (2017)"},{"key":"3_CR68","doi-asserted-by":"crossref","unstructured":"Rathi, N., Roy, K.: Diet-SNN: a low-latency spiking neural network with direct input encoding and leakage and threshold optimization. IEEE Trans. Neural Net. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3111897"},{"key":"3_CR69","unstructured":"Rathi, N., Srinivasan, G., Panda, P., Roy, K.: Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. arXiv preprint arXiv:2005.01807 (2020)"},{"key":"3_CR70","doi-asserted-by":"crossref","unstructured":"Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 4780\u20134789 (2019)","DOI":"10.1609\/aaai.v33i01.33014780"},{"issue":"7784","key":"3_CR71","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s41586-019-1677-2","volume":"575","author":"K Roy","year":"2019","unstructured":"Roy, K., Jaiswal, A., Panda, P.: Towards spike-based machine intelligence with neuromorphic computing. Nature 575(7784), 607\u2013617 (2019)","journal-title":"Nature"},{"key":"3_CR72","doi-asserted-by":"publisher","first-page":"682","DOI":"10.3389\/fnins.2017.00682","volume":"11","author":"B Rueckauer","year":"2017","unstructured":"Rueckauer, B., Lungu, I.A., Hu, Y., Pfeiffer, M., Liu, S.C.: Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 682 (2017)","journal-title":"Front. Neurosci."},{"key":"3_CR73","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3389\/fnins.2019.00095","volume":"13","author":"A Sengupta","year":"2019","unstructured":"Sengupta, A., Ye, Y., Wang, R., Liu, C., Roy, K.: Going deeper in spiking neural networks: VGG and residual architectures. Front. Neurosci. 13, 95 (2019)","journal-title":"Front. Neurosci."},{"key":"3_CR74","unstructured":"Shrestha, S.B., Orchard, G.: SLAYER: spike layer error reassignment in time. arXiv preprint arXiv:1810.08646 (2018)"},{"key":"3_CR75","unstructured":"Shu, Y., Wang, W., Cai, S.: Understanding architectures learnt by cell-based neural architecture search. arXiv preprint arXiv:1909.09569 (2019)"},{"key":"3_CR76","unstructured":"Siems, J., Zimmer, L., Zela, A., Lukasik, J., Keuper, M., Hutter, F.: NAS-bench-301 and the case for surrogate benchmarks for neural architecture search. arXiv preprint arXiv:2008.09777 (2020)"},{"key":"3_CR77","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"3_CR78","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820\u20132828 (2019)","DOI":"10.1109\/CVPR.2019.00293"},{"key":"3_CR79","doi-asserted-by":"crossref","unstructured":"Venkatesha, Y., Kim, Y., Tassiulas, L., Panda, P.: Federated learning with spiking neural networks. arXiv preprint arXiv:2106.06579 (2021)","DOI":"10.1109\/TSP.2021.3121632"},{"key":"3_CR80","doi-asserted-by":"crossref","unstructured":"Wu, B., et al.: FBNet: hardware-aware efficient convnet design via differentiable neural architecture search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10734\u201310742 (2019)","DOI":"10.1109\/CVPR.2019.01099"},{"key":"3_CR81","unstructured":"Wu, H., et al.: Training spiking neural networks with accumulated spiking flow. IJO 1(1) (2021)"},{"key":"3_CR82","unstructured":"Wu, J., Chua, Y., Zhang, M., Li, G., Li, H., Tan, K.C.: A tandem learning rule for effective training and rapid inference of deep spiking neural networks. arXiv e-prints pp. arXiv-1907 (2019)"},{"key":"3_CR83","unstructured":"Wu, J., Xu, C., Zhou, D., Li, H., Tan, K.C.: Progressive tandem learning for pattern recognition with deep spiking neural networks. arXiv preprint arXiv:2007.01204 (2020)"},{"key":"3_CR84","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3389\/fnins.2018.00331","volume":"12","author":"Y Wu","year":"2018","unstructured":"Wu, Y., Deng, L., Li, G., Zhu, J., Shi, L.: Spatio-temporal backpropagation for training high-performance spiking neural networks. Front. Neurosci. 12, 331 (2018)","journal-title":"Front. Neurosci."},{"key":"3_CR85","doi-asserted-by":"crossref","unstructured":"Wu, Y., Deng, L., Li, G., Zhu, J., Xie, Y., Shi, L.: Direct training for spiking neural networks: faster, larger, better. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1311\u20131318 (2019)","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"3_CR86","unstructured":"Xie, S., Zheng, H., Liu, C., Lin, L.: SNAS: stochastic neural architecture search. arXiv preprint arXiv:1812.09926 (2018)"},{"key":"3_CR87","unstructured":"Xiong, H., Huang, L., Yu, M., Liu, L., Zhu, F., Shao, L.: On the number of linear regions of convolutional neural networks. In: International Conference on Machine Learning, pp. 10514\u201310523. PMLR (2020)"},{"key":"3_CR88","unstructured":"Xu, J., Zhao, L., Lin, J., Gao, R., Sun, X., Yang, H.: KNAS: green neural architecture search. In: International Conference on Machine Learning, pp. 11613\u201311625. PMLR (2021)"},{"key":"3_CR89","doi-asserted-by":"crossref","unstructured":"Xu, L., et al.: ViPNAS: efficient video pose estimation via neural architecture search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16072\u201316081 (2021)","DOI":"10.1109\/CVPR46437.2021.01581"},{"key":"3_CR90","doi-asserted-by":"crossref","unstructured":"Yan, Z., Dai, X., Zhang, P., Tian, Y., Wu, B., Feiszli, M.: FP-NAS: fast probabilistic neural architecture search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15139\u201315148 (2021)","DOI":"10.1109\/CVPR46437.2021.01489"},{"key":"3_CR91","doi-asserted-by":"crossref","unstructured":"Yang, T.J., Liao, Y.L., Sze, V.: NetAdaptV2: efficient neural architecture search with fast super-network training and architecture optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2402\u20132411 (2021)","DOI":"10.1109\/CVPR46437.2021.00243"},{"key":"3_CR92","doi-asserted-by":"crossref","unstructured":"Yang, Y., You, S., Li, H., Wang, F., Qian, C., Lin, Z.: Towards improving the consistency, efficiency, and flexibility of differentiable neural architecture search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6667\u20136676 (2021)","DOI":"10.1109\/CVPR46437.2021.00660"},{"key":"3_CR93","doi-asserted-by":"crossref","unstructured":"Yang, Z., et al.: HourNAS: extremely fast neural architecture search through an hourglass lens. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10896\u201310906 (2021)","DOI":"10.1109\/CVPR46437.2021.01075"},{"key":"3_CR94","doi-asserted-by":"crossref","unstructured":"Yao, M., et al.: Temporal-wise attention spiking neural networks for event streams classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10221\u201310230 (2021)","DOI":"10.1109\/ICCV48922.2021.01006"},{"key":"3_CR95","unstructured":"Ying, C., Klein, A., Christiansen, E., Real, E., Murphy, K., Hutter, F.: NAS-bench-101: towards reproducible neural architecture search. In: International Conference on Machine Learning, pp. 7105\u20137114. PMLR (2019)"},{"key":"3_CR96","doi-asserted-by":"publisher","first-page":"665","DOI":"10.3389\/fnins.2018.00665","volume":"12","author":"A Yousefzadeh","year":"2018","unstructured":"Yousefzadeh, A., Stromatias, E., Soto, M., Serrano-Gotarredona, T., Linares-Barranco, B.: On practical issues for stochastic STDP hardware with 1-bit synaptic weights. Front. Neurosci. 12, 665 (2018)","journal-title":"Front. Neurosci."},{"key":"3_CR97","doi-asserted-by":"crossref","unstructured":"Zeng, D., Huang, Y., Bao, Q., Zhang, J., Su, C., Liu, W.: Neural architecture search for joint human parsing and pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11385\u201311394 (2021)","DOI":"10.1109\/ICCV48922.2021.01119"},{"key":"3_CR98","unstructured":"Zhang, W., Li, P.: Spike-train level backpropagation for training deep recurrent spiking neural networks. arXiv preprint arXiv:1908.06378 (2019)"},{"key":"3_CR99","unstructured":"Zhang, W., Li, P.: Temporal spike sequence learning via backpropagation for deep spiking neural networks. arXiv preprint arXiv:2002.10085 (2020)"},{"issue":"9","key":"3_CR100","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1109\/TPAMI.2020.3020300","volume":"43","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Huang, Z., Wang, N., Xiang, S., Pan, C.: you only search once: single shot neural architecture search via direct sparse optimization. IEEE Trans. Pattern Anal. Mach. Intell. 43(9), 2891\u20132904 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR101","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: DCNAS: densely connected neural architecture search for semantic image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13956\u201313967 (2021)","DOI":"10.1109\/CVPR46437.2021.01374"},{"key":"3_CR102","doi-asserted-by":"crossref","unstructured":"Zhang, X., Hou, P., Zhang, X., Sun, J.: Neural architecture search with random labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10907\u201310916 (2021)","DOI":"10.1109\/CVPR46437.2021.01076"},{"key":"3_CR103","unstructured":"Zhao, Y., Wang, L., Tian, Y., Fonseca, R., Guo, T.: Few-shot neural architecture search. In: International Conference on Machine Learning, pp. 12707\u201312718. PMLR (2021)"},{"key":"3_CR104","doi-asserted-by":"crossref","unstructured":"Zheng, H., Wu, Y., Deng, L., Hu, Y., Li, G.: Going deeper with directly-trained larger spiking neural networks. arXiv preprint arXiv:2011.05280 (2020)","DOI":"10.1609\/aaai.v35i12.17320"},{"key":"3_CR105","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Yan, J., Wu, W., Shao, J., Liu, C.L.: Practical block-wise neural network architecture generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2423\u20132432 (2018)","DOI":"10.1109\/CVPR.2018.00257"},{"key":"3_CR106","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)"},{"key":"3_CR107","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697\u20138710 (2018)","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20053-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:23:49Z","timestamp":1667665429000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20053-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200526","9783031200533"],"references-count":107,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20053-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}