Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision
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Elisabetta Chicca | Alexander Kugele | Michael Pfeiffer | Thomas Pfeil | Michael Pfeiffer | Thomas Pfeil | E. Chicca | Alexander Kugele
[1] Giacomo Indiveri,et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses , 2015, Front. Neurosci..
[2] Kostas Daniilidis,et al. Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks , 2020, ECCV.
[3] Jan Köhler,et al. The streaming rollout of deep networks - towards fully model-parallel execution , 2018, NeurIPS.
[4] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Tobi Delbrück,et al. A Low Power, Fully Event-Based Gesture Recognition System , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Thomas Serre,et al. Stable and expressive recurrent vision models , 2020, NeurIPS.
[7] Surya Ganguli,et al. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks , 2017, Neural Computation.
[8] Davide Scaramuzza,et al. End-to-End Learning of Representations for Asynchronous Event-Based Data , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] S. Furber,et al. Comparison of Artificial and Spiking Neural Networks on Digital Hardware , 2021, Frontiers in Neuroscience.
[10] Juan Martín Carpio Valadez,et al. Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems , 2019, Comput. Intell. Neurosci..
[11] Ryad Benosman,et al. Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities , 2018, Frontiers in Neuroscience.
[12] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[13] Tobi Delbruck,et al. A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor , 2014, IEEE Journal of Solid-State Circuits.
[14] Davide Scaramuzza,et al. Dynamic obstacle avoidance for quadrotors with event cameras , 2020, Science Robotics.
[15] Bernabé Linares-Barranco,et al. A 128$\,\times$ 128 1.5% Contrast Sensitivity 0.9% FPN 3 µs Latency 4 mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Preamplifiers , 2013, IEEE Journal of Solid-State Circuits.
[16] Kaushik Roy,et al. DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks , 2020, ArXiv.
[17] Shih-Chii Liu,et al. Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks , 2016, SAC.
[18] Mattias Nilsson,et al. Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors , 2019, Frontiers in Neuroscience.
[19] Raquel Urtasun,et al. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks , 2016, NIPS.
[20] Hong Wang,et al. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.
[21] Sumon Kumar Bose,et al. Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering , 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.
[22] Garrick Orchard,et al. HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Shih-Chii Liu,et al. Neuromorphic sensory systems , 2010, Current Opinion in Neurobiology.
[24] Lei Deng,et al. Direct Training for Spiking Neural Networks: Faster, Larger, Better , 2018, AAAI.
[25] Gregory Cohen,et al. Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades , 2015, Front. Neurosci..
[26] Elisabetta Chicca,et al. Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks , 2020, Frontiers in Neuroscience.
[27] D. Lewis,et al. Horizontal synaptic connections in monkey prefrontal cortex: an in vitro electrophysiological study. , 2000, Cerebral cortex.
[28] Tobi Delbrück,et al. The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM , 2016, Int. J. Robotics Res..
[29] T. Delbruck,et al. A 128 128 120 dB 15 s Latency Asynchronous Temporal Contrast Vision Sensor , 2006 .
[30] Jochen Triesch,et al. Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor , 2020, International Conference on Artificial Neural Networks.
[31] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[32] Hang Su,et al. Neuromorphic Visual Odometry System For Intelligent Vehicle Application With Bio-inspired Vision Sensor , 2019, 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO).
[33] Johannes Schemmel,et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[34] Wulfram Gerstner,et al. Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition , 2014 .
[35] Gert Cauwenberghs,et al. Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain , 2018, Front. Neurosci..
[36] Davide Scaramuzza,et al. Video to Events: Recycling Video Datasets for Event Cameras , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Romain Brette,et al. Brian 2: an intuitive and efficient neural simulator , 2019, bioRxiv.
[38] Johannes Schemmel,et al. Surrogate gradients for analog neuromorphic computing , 2020, Proceedings of the National Academy of Sciences.
[39] Davide Scaramuzza,et al. Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios , 2017, IEEE Robotics and Automation Letters.
[40] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[41] Tobi Delbrück,et al. Training Deep Spiking Neural Networks Using Backpropagation , 2016, Front. Neurosci..
[42] Shih-Chii Liu,et al. Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification , 2017, Front. Neurosci..
[43] Andreas Hartel,et al. Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate , 2019, International Symposium on Circuits and Systems.
[44] 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.
[45] Etienne Perot,et al. Learning to Detect Objects with a 1 Megapixel Event Camera , 2020, NeurIPS.
[46] William Bialek,et al. Spikes: Exploring the Neural Code , 1996 .
[47] Garrick Orchard,et al. SLAYER: Spike Layer Error Reassignment in Time , 2018, NeurIPS.
[48] Ryad Benosman,et al. HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[49] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[50] Kaushik Roy,et al. Going Deeper in Spiking Neural Networks: VGG and Residual Architectures , 2018, Front. Neurosci..
[51] Jim D. Garside,et al. Overview of the SpiNNaker System Architecture , 2013, IEEE Transactions on Computers.
[52] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[53] Timothée Masquelier,et al. STDP-based spiking deep neural networks for object recognition , 2016, Neural Networks.
[54] Vladlen Koltun,et al. Events-To-Video: Bringing Modern Computer Vision to Event Cameras , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[55] Daniel Matolin,et al. A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS , 2011, IEEE Journal of Solid-State Circuits.
[56] Chiara Bartolozzi,et al. Event-Based Vision: A Survey , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Andrew S. Cassidy,et al. A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.