Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridization
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[1] J. Yang,et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.
[2] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[3] Jyrki Alakuijala,et al. Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function , 2019, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[4] M. Marinella,et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. , 2017, Nature materials.
[5] Emre Neftci,et al. Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks , 2019, IEEE Signal Processing Magazine.
[6] Robert A. Legenstein,et al. Long short-term memory and Learning-to-learn in networks of spiking neurons , 2018, NeurIPS.
[7] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[8] Chris Eliasmith,et al. Spiking Deep Networks with LIF Neurons , 2015, ArXiv.
[9] Mingguo Zhao,et al. Towards artificial general intelligence with hybrid Tianjic chip architecture , 2019, Nature.
[10] Hesham Mostafa,et al. Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks , 2019, IEEE Signal Processing Magazine.
[11] Bernabé Linares-Barranco,et al. On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..
[12] Bernabé Linares-Barranco,et al. On neuromorphic spiking architectures for asynchronous STDP memristive systems , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[13] A. Krizhevsky. Convolutional Deep Belief Networks on CIFAR-10 , 2010 .
[14] Kaushik Roy,et al. Encoding Neural and Synaptic Functionalities in Electron Spin: A Pathway to Efficient Neuromorphic Computing , 2017, ArXiv.
[15] Kaushik Roy,et al. Enabling Spike-based Backpropagation in State-of-the-art Deep Neural Network Architectures , 2019 .
[16] Jason M. Allred,et al. ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks , 2017, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[17] Kaushik Roy,et al. RESPARC: A reconfigurable and energy-efficient architecture with Memristive Crossbars for deep Spiking Neural Networks , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).
[18] Ying Chen,et al. Direct training based spiking convolutional neural networks for object recognition , 2019, ArXiv.
[19] Kaushik Roy,et al. Unsupervised regenerative learning of hierarchical features in Spiking Deep Networks for object recognition , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[20] Matthew Cook,et al. Unsupervised learning of digit recognition using spike-timing-dependent plasticity , 2015, Front. Comput. Neurosci..
[21] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, NIPS.
[22] Kaushik Roy,et al. Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures , 2019, Frontiers in Neuroscience.
[23] Kaushik Roy,et al. Going Deeper in Spiking Neural Networks: VGG and Residual Architectures , 2018, Front. Neurosci..
[24] Tobi Delbruck,et al. Real-time classification and sensor fusion with a spiking deep belief network , 2013, Front. Neurosci..
[25] Craig M. Vineyard,et al. Training deep neural networks for binary communication with the Whetstone method , 2019 .
[26] Saeed Reza Kheradpisheh,et al. S4NN: temporal backpropagation for spiking neural networks with one spike per neuron , 2020, Int. J. Neural Syst..
[27] Timothée Masquelier,et al. Competitive STDP-Based Spike Pattern Learning , 2009, Neural Computation.
[28] Gopalakrishnan Srinivasan,et al. Deep Spiking Convolutional Neural Network Trained With Unsupervised Spike-Timing-Dependent Plasticity , 2019, IEEE Transactions on Cognitive and Developmental Systems.
[29] G. Indiveri,et al. Neuromorphic architectures for spiking deep neural networks , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).
[30] Gopalakrishnan Srinivasan,et al. Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning , 2018, Front. Neurosci..
[31] Deepak Khosla,et al. Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition , 2014, International Journal of Computer Vision.
[32] Aran Nayebi,et al. CORnet: Modeling the Neural Mechanisms of Core Object Recognition , 2018, bioRxiv.
[33] Tobi Delbrück,et al. Training Deep Spiking Neural Networks Using Backpropagation , 2016, Front. Neurosci..
[34] Chris Eliasmith,et al. A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions , 2020, ArXiv.
[35] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[36] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Kaushik Roy,et al. ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing , 2019, Front. Neurosci..
[38] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[39] Timothée Masquelier,et al. Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..
[40] Matthew Cook,et al. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[41] Kaushik Roy,et al. Hybrid Spintronic-CMOS Spiking Neural Network With On-Chip Learning: Devices, Circuits and Systems , 2015, ArXiv.
[42] Paul J. Werbos,et al. Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.
[43] Kaushik Roy,et al. STDP-based Unsupervised Feature Learning using Convolution-over-time in Spiking Neural Networks for Energy-Efficient Neuromorphic Computing , 2018, ACM J. Emerg. Technol. Comput. Syst..
[44] Kaushik Roy,et al. Towards spike-based machine intelligence with neuromorphic computing , 2019, Nature.
[45] Sen Lu,et al. Exploring the Connection Between Binary and Spiking Neural Networks , 2020, Frontiers in Neuroscience.
[46] Hesham Mostafa,et al. Supervised Learning Based on Temporal Coding in Spiking Neural Networks , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[47] Michael Pfeiffer,et al. Deep Learning With Spiking Neurons: Opportunities and Challenges , 2018, Front. Neurosci..
[48] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[50] Giacomo Indiveri,et al. Frontiers in Neuromorphic Engineering , 2011, Front. Neurosci..
[51] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[52] S. Thorpe,et al. STDP-based spiking deep convolutional neural networks for object recognition , 2018 .
[53] Eunho Yang,et al. DropMax: Adaptive Variational Softmax , 2017, NeurIPS.