Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networks
暂无分享,去创建一个
[1] Ardavan Pedram,et al. Griffin: Rethinking Sparse Optimization for Deep Learning Architectures , 2021, 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA).
[2] Shih-Chii Liu,et al. Temporal Pattern Coding in Deep Spiking Neural Networks , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).
[3] K. Salama,et al. Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems , 2021, Frontiers in Neuroscience.
[4] Ryan Kastner,et al. S2N2: A FPGA Accelerator for Streaming Spiking Neural Networks , 2021, FPGA.
[5] Qinru Qiu,et al. Encoding, Model, and Architecture: Systematic Optimization for Spiking Neural Network in FPGAs , 2020, 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD).
[6] Kaushik Roy,et al. Deep Spiking Neural Network: Energy Efficiency Through Time Based Coding , 2020, ECCV.
[7] Rajeev Balasubramonian,et al. SpinalFlow: An Architecture and Dataflow Tailored for Spiking Neural Networks , 2020, 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA).
[8] Weixia Xu,et al. SIES: A Novel Implementation of Spiking Convolutional Neural Network Inference Engine on Field-Programmable Gate Array , 2020, Journal of Computer Science and Technology.
[9] Bernabe Linares-Barranco,et al. Asynchronous Spiking Neurons, the Natural Key to Exploit Temporal Sparsity , 2019, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[10] Kay Chen Tan,et al. A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[11] Lei Wang,et al. A Systolic SNN Inference Accelerator and its Co-optimized Software Framework , 2019, ACM Great Lakes Symposium on VLSI.
[12] Shasha Guo,et al. ASIE: An Asynchronous SNN Inference Engine for AER Events Processing , 2019, 2019 25th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC).
[13] Edith Beigné,et al. Spiking Neural Networks Hardware Implementations and Challenges , 2019, ACM J. Emerg. Technol. Comput. Syst..
[14] Kaushik Roy,et al. Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures , 2019, Frontiers in Neuroscience.
[15] Ricardo Tapiador-Morales,et al. Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA , 2019, IEEE Transactions on Biomedical Circuits and Systems.
[16] 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.
[17] Tianshi Chen,et al. Cambricon-S: Addressing Irregularity in Sparse Neural Networks through A Cooperative Software/Hardware Approach , 2018, 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[18] Vivienne Sze,et al. Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices , 2018, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[19] Shih-Chii Liu,et al. Conversion of analog to spiking neural networks using sparse temporal coding , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).
[20] Timothée Masquelier,et al. Deep Learning in Spiking Neural Networks , 2018, Neural Networks.
[21] Kaushik Roy,et al. Going Deeper in Spiking Neural Networks: VGG and Residual Architectures , 2018, Front. Neurosci..
[22] Hong Wang,et al. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.
[23] Bo Chen,et al. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Shih-Chii Liu,et al. Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification , 2017, Front. Neurosci..
[25] William J. Dally,et al. SCNN: An accelerator for compressed-sparse convolutional neural networks , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[26] Gert Cauwenberghs,et al. Fast classification using sparsely active spiking networks , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).
[27] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[28] Vivienne Sze,et al. Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.
[29] V. Sze,et al. Hardware for machine learning: Challenges and opportunities , 2016, 2018 IEEE Custom Integrated Circuits Conference (CICC).
[30] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Bernard Brezzo,et al. TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip , 2015, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[33] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[34] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[35] Mark Horowitz,et al. 1.1 Computing's energy problem (and what we can do about it) , 2014, 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC).
[36] Nicolas Brunel,et al. Sensory neural codes using multiplexed temporal scales , 2010, Trends in Neurosciences.
[37] Eugene M. Izhikevich,et al. Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.
[38] Arnaud Delorme,et al. Spike-based strategies for rapid processing , 2001, Neural Networks.
[39] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..