Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Computer Vision

08.04.2024

Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration

verfasst von: Yuhang Li, Shikuang Deng, Xin Dong, Shi Gu

Erschienen in: International Journal of Computer Vision

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtained by converting from pre-trained Artificial Neural Networks (ANNs) by replacing the non-linear activation with spiking neurons without changing the parameters. In this work, we argue that simply copying and pasting the weights of ANN to SNN inevitably results in activation mismatch, especially for ANNs that are trained with batch normalization (BN) layers. To tackle the activation mismatch issue, we first provide a theoretical analysis by decomposing local layer-wise conversion error, and then quantitatively measure how this error propagates throughout the layers using the second-order analysis. Motivated by the theoretical results, we propose a set of layer-wise parameter calibration algorithms, which adjusts the parameters to minimize the activation mismatch. To further remove the dependency on data, we propose a privacy-preserving conversion regime by distilling synthetic data from source ANN and using it to calibrate the SNN. Extensive experiments for the proposed algorithms are performed on modern architectures and large-scale tasks including ImageNet classification and MS COCO detection. We demonstrate that our method can handle the SNN conversion and effectively preserve high accuracy even in 32 time steps. For example, our calibration algorithms can increase up to 63% accuracy when converting MobileNet against baselines.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
Literatur
Akopyan, F., Sawada, J., Cassidy, A., Alvarez-Icaza, R., Arthur, J., Merolla, P., Imam, N., Nakamura, Y., Datta, P., Nam, G. J., Taba, B., Beakes, M. P., Brezzo, B., Kuang, J. B., Manohar, R., Risk, W. P., Jackson, B. L., & Modha, D. S. (2015). Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 34(10), 1537–1557.CrossRef
Barbi, M., Chillemi, S., Di Garbo, A., & Reale, L. (2003). Stochastic resonance in a sinusoidally forced LIF model with noisy threshold. Biosystems, 71(1–2), 23–28.CrossRef
Bengio, Y., Léonard, N., & Courville, A. (2013). Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:​1308.​3432.
Bi, G., & Poo, M. (1998). Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience, 18(24), 10464–10472.CrossRef
Botev, A., Ritter, H., & Barber, D. (2017). Practical gauss-newton optimisation for deep learning. In International conference on machine learning (pp. 557–565). PMLR.
Bu, T., Fang, W., Ding, J., Dai, P., Yu, Z., & Huang, T. (2021). Optimal ann-snn conversion for high-accuracy and ultra-low-latency spiking neural networks. In International conference on learning representations.
Bu, T., Ding, J., Yu, Z., & Huang, T. (2022). Optimized potential initialization for low-latency spiking neural networks. In Proceedings of the AAAI conference on artificial intelligence (pp. 11–20).
Chen, H., Wang, Y., Xu, C., Yang, Z., Liu, C., Shi, B., Xu, C., Xu, C., & Tian, Q. (2019). Data-free learning of student networks. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3514–3522).
Chowdhury, S. S., Rathi, N., & Roy, K. (2021). One timestep is all you need: Training spiking neural networks with ultra low latency. arXiv preprint arXiv:​2110.​05929.
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., Valov, I., Milano, G., Ricciardi, C., Liang, S.-J., Miao, F., Lanza, M., Quill, T. J., Keene, S. T., Salleo, A., & Pryds, N. (2022). 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Computing and Engineering, 2(2), 022501.CrossRef
Cox, D. D., & Dean, T. (2014). Neural networks and neuroscience-inspired computer vision. Current Biology, 24(18), R921–R929.CrossRef
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2019). Autoaugment: Learning augmentation strategies from data. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 113–123).
Davies, M., Srinivasa, N., Lin, T. H., Chinya, G., Cao, Y., Choday, S. H., Dimou, G., Joshi, P., Imam, N., Jain, S., Liao, Y., Lin, C.-K., Lines, A., Liu, R., Mathaikutty, D., McCoy, S., Paul, A., Tse, J., Venkataramanan, G., & Wang, H. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 38(1), 82–99.CrossRef
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255). IEEE.
Deng, L., Wu, Y., Hu, X., Liang, L., Ding, Y., Li, G., Zhao, G., Li, P., & Xie, Y. (2020). Rethinking the performance comparison between SNNS and ANNS. Neural Networks, 121, 294–307.CrossRef
DeVries, T., & Taylor, G. W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:​1708.​04552.
Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S. C., & Pfeiffer, M. (2015). Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In 2015 International joint conference on neural networks (IJCNN) (pp. 1–8). IEEE.
Diehl, P. U., Zarrella, G., Cassidy, A., Pedroni, B. U., & Neftci, E. (2016). Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In 2016 IEEE international conference on rebooting computing (ICRC) (pp. 1–8). IEEE.
Dong, X., Chen, S., & Pan, S. (2017a). Learning to prune deep neural networks via layer-wise optimal brain surgeon. Advances in Neural Information Processing Systems,30.
Dong, X., Chen, S., & Pan, S. (2017b). Learning to prune deep neural networks via layer-wise optimal brain surgeon. Advances in Neural Information Processing Systems.
Dong, Z., Yao, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2019). Hawq: Hessian aware quantization of neural networks with mixed-precision. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 293–302).
Fang, W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., & Tian, Y. (2021). Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 2661–2671).
Furber, S. B., Galluppi, F., Temple, S., et al. (2014). The spinnaker project. Proceedings of the IEEE, 102(5), 652–665.CrossRef
Gu, P., Xiao, R., Pan, G., & Tang, H. (2019). STCA: Spatio-temporal credit assignment with delayed feedback in deep spiking neural networks. In IJCAI (Vol. 15, pp. 1366–1372).
Han, B., & Roy, K. (2020). Deep spiking neural network: Energy efficiency through time based coding. In European conference on computer vision.
Han, B., Srinivasan, G., & Roy, K. (2020). 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. 13558–13567).
Hassibi, B., & Stork, D. G. (1993). Second order derivatives for network pruning: Optimal brain surgeon. Advances in Neural Information Processing Systems, 5, 164–171.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition.
He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., & Li, M. (2019). Bag of tricks for image classification with convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 558–567).
Hebb, D. O. (2005). The organization of behavior: A neuropsychological theory. Psychology Press.CrossRef
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544.CrossRef
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:​1704.​04861.
Iakymchuk, T., Rosado-Muñoz, A., Guerrero-Martínez, J. F., Bataller-Mompeán, M., & Francés-Villora, J. V. (2015). Simplified spiking neural network architecture and stdp learning algorithm applied to image classification. EURASIP Journal on Image and Video Processing, 1, 1–11.
Ikegawa, S. I., Saiin, R., Sawada, Y., & Natori, N. (2022). Rethinking the role of normalization and residual blocks for spiking neural networks. Sensors, 22(8), 2876.CrossRef
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448–456). PMLR.
Iyer, L. R., & Chua, Y. (2020). Classifying neuromorphic datasets with tempotron and spike timing dependent plasticity. In 2020 international joint conference on neural networks (IJCNN) (pp. 1–8). IEEE.
Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569–1572.MathSciNetCrossRef
Kheradpisheh, S. R., Ganjtabesh, M., Thorpe, S. J., & Masquelier, T. (2018). STDP-based spiking deep convolutional neural networks for object recognition. Neural Networks, 99, 56–67.CrossRef
Kim, S., Park, S., Na, B., & Yoon, S. (2020). Spiking-yolo: Spiking neural network for energy-efficient object detection. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 07, pp. 11270–11277).
Kim, Y., & Panda, P. (2021). Revisiting batch normalization for training low-latency deep spiking neural networks from scratch. Frontiers in Neuroscience, 15(773), 954.
Kim, Y., Li, Y., Park, H., Venkatesha, Y., & Panda, P. (2022). Neural architecture search for spiking neural networks. In European conference on computer vision (pp. 36–56). Cham: Springer Nature Switzerland.
Lee, C., Panda, P., Srinivasan, G., & Roy, K. (2018). Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised fine-tuning. Frontiers in Neuroscience, 12, 373945.CrossRef
Lee, C., Sarwar, S. S., Panda, P., Srinivasan, G., & Roy, K. (2020). Enabling spike-based backpropagation for training deep neural network architectures. Frontiers in Neuroscience, 14, 497482.CrossRef
Lee, J. H., Delbruck, T., & Pfeiffer, M. (2016). Training deep spiking neural networks using backpropagation. Frontiers in Neuroscience, 10, 508.CrossRef
Li S. L., & Li, J. P. (2019). Research on learning algorithm of spiking neural network. In 2019 16th international computer conference on wavelet active media technology and information processing (pp. 45–48). IEEE.
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.CrossRef
Li, Y., & Zeng, Y. (2022). Efficient and accurate conversion of spiking neural network with burst spikes. arXiv preprint arXiv:​2204.​13271.
Li, Y., Deng, S., Dong, X., Gong, R., & Gu, S. (2021). A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration. In International conference on machine learning (pp. 6316–6325). PMLR.
Li, Y., Guo, Y., Zhang, S., Deng, S., Hai, Y., & Gu, S. (2021). Differentiable spike: Rethinking gradient-descent for training spiking neural networks. Advances in Neural Information Processing Systems, 34, 23426–23439.
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V 13 (pp. 740–755). Springer International Publishing.
Lin, T. Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117–2125).
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980–2988).
Liu, Y. H., & Wang, X. J. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 10(1), 25–45.CrossRef
Liu, Z., Wu, Z., Gan, C., Zhu, L., & Han, S. (2020). Datamix: Efficient privacy-preserving edge-cloud inference. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16 (pp. 578–595). Springer International Publishing.
Lobov, S. A., Mikhaylov, A. N., Shamshin, M., Makarov, V. A., & Kazantsev, V. B. (2020). Spatial properties of STDP in a self-learning spiking neural network enable controlling a mobile robot. Frontiers in Neuroscience, 14, 491341.CrossRef
Meng, Q., Xiao, M., Yan, S., Wang, Y., Lin, Z., & Luo, Z. Q. (2022). Training high-performance low-latency spiking neural networks by differentiation on spike representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 12444–12453).
Miquel, J. R., Tolu, S., Scholler, F. E., & Galeazzi, R. (2021). Retinanet object detector based on analog-to-spiking neural network conversion. In 2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI) (pp. 201–205).
Neftci, E. O., Mostafa, H., & Zenke, F. (2019). Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Processing Magazine, 36(6), 51–63.CrossRef
Pei, J., Deng, L., Song, S., Zhao, M., Zhang, Y., Wu, S., Wang, Y., Wu, Y., Yang, Z., Ma, C., Li, G., Han, W., Li, H., Wu, H., Zhao, R., Xie, Y., & Shi, L. P. (2019). Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 572(7767), 106–111.CrossRef
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., & Dollar, P. (2020). Designing network design spaces. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10428–10436).
Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). Xnor-net: Imagenet classification using binary convolutional neural networks. In European conference on computer vision (pp. 525–542). Cham: Springer International Publishing.
Rathi, N., & Roy, K. (2021). Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization. IEEE Transactions on Neural Networks and Learning Systems, 34(6), 3174–3182.CrossRef
Rathi, N., Srinivasan, G., Panda, P., & Roy, K. (2019). Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. In International conference on learning representations.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28.
Roy, D., Chakraborty, I., & Roy, K. (2019). Scaling deep spiking neural networks with binary stochastic activations. In 2019 IEEE International Conference on Cognitive Computing (ICCC) (pp. 50–58). IEEE.
Roy, K., Jaiswal, A., & Panda, P. (2019). Towards spike-based machine intelligence with neuromorphic computing. Nature, 575(7784), 607–617.CrossRef
Rueckauer, B., Lungu, I. A., Hu, Y., & Pfeiffer, M. (2016). Theory and tools for the conversion of analog to spiking convolutional neural networks. arXiv:​ Statistics/​Machine Learning (1612.04052).
Rueckauer, B., Lungu, I. A., Hu, Y., Pfeiffer, M., & Liu, S. C. (2017). Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Frontiers in Neuroscience, 11, 294078.CrossRef
Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. (2018). How does batch normalization help optimization?. Advances in Neural Information Processing Systems, 31.
Sengupta, A., Ye, Y., Wang, R., Liu, C., & Roy, K. (2019). Going deeper in spiking neural networks: VGG and residual architectures. Frontiers in Neuroscience, 13, 425055.CrossRef
Shrestha, S. B., & Orchard, G. (2018). Slayer: Spike layer error reassignment in time. Advances in Neural Information Processing Systems, 31, 1412–1421.
Silver, D., Huang, A., Maddison, C. J., Guez, Arthur, Sifre, Laurent, van den Driessche, George, Schrittwieser, Julian, Antonoglou, Ioannis, Panneershelvam, Veda, Lanctot, Marc, Dieleman, Sander, Grewe, Dominik, Nham, John, Kalchbrenner, Nal, Sutskever, Ilya, Lillicrap, Timothy, Leach, Madeleine, Kavukcuoglu, Koray, Graepel, Thore, & Hassabis, Demis. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489.CrossRef
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556.
Suetake, K., Ikegawa, S. I., Saiin, R., & Sawada, Y. (2023). S3NN: Time step reduction of spiking surrogate gradients for training energy efficient single-step spiking neural networks. Neural Networks, 159, 208–219.CrossRef
Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329.CrossRef
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826).
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2820–2828).
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., & Maida, A. (2019). Deep learning in spiking neural networks. Neural Networks, 111, 47–63.CrossRef
Theis, L., Korshunova, I., Tejani, A., & Huszar, F. (2018). Faster gaze prediction with dense networks and fisher pruning. arXiv preprint arXiv:​1801.​05787.
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Junhyuk, O., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J. P., Jaderberg, M., & Silver, D. (2019). Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782), 350–354.CrossRef
Wang, Y., Zhang, M., Chen, Y., & Qu, H. (2022). Signed neuron with memory: Towards simple, accurate and high-efficient ANN-SNN conversion. In International joint conference on artificial intelligence (pp. 2501–2508).
Wu, J., Chua, Y., Zhang, M., Li, G., Li, H., & Tan, K. C. (2021). A tandem learning rule for effective training and rapid inference of deep spiking neural networks. IEEE Transactions on Neural Networks and Learning Systems, 34(1), 446–460.CrossRef
Wu, J., Xu, C., Han, X., Zhou, D., Zhang, M., Li, H., & Tan, K. C. (2021). Progressive tandem learning for pattern recognition with deep spiking neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7824–7840.CrossRef
Wu, Y., Deng, L., Li, G., & Shi, L. (2018). Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in Neuroscience, 12, 331.CrossRef
Wu, Y., Zhao, R., Zhu, J., Chen, F., Xu, M., Li, G., Song, S., Deng, L., Wang, G., Zheng, H., Pei, J., Zhang, Y., Zhao, M., & Shi, L. (2022). Brain-inspired global-local learning incorporated with neuromorphic computing. Nature Communications, 13(1), 1–14.
Xiao, M., Meng, Q., Zhang, Z., Wang, Y., & Lin, Z. (2021). Training feedback spiking neural networks by implicit differentiation on the equilibrium state. Advances in Neural Information Processing Systems, 34, 14516–14528.
Xiao, M., Meng, Q., Zhang, Z., He, D., & Lin, Z. (2022). Online training through time for spiking neural networks. Advances in Neural Information Processing Systems, 35, 20717-20730.
Yin, H., Molchanov, P., Alvarez, J. M., Li, Z., Mallya, A., Hoiem, D., Jha, N. K. & Kautz, J. (2020). Dreaming to distill: Data-free knowledge transfer via deepinversion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8715–8724).
Zheng, H., Wu, Y., Deng, L., Hu, Y., & Li, G. (2021). Going deeper with directly-trained larger spiking neural networks. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 12, pp. 11062–11070).
Metadaten
Titel
Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration
verfasst von
Yuhang Li
Shikuang Deng
Xin Dong
Shi Gu
Publikationsdatum
08.04.2024
Verlag
Springer US
Erschienen in
International Journal of Computer Vision
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-024-02046-2

Premium Partner

    Bildnachweise