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The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware [article]

Alpha Renner, Forrest Sheldon, Anatoly Zlotnik, Louis Tao, Andrew Sornborger
2021 arXiv   pre-print
In this study, we present a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing, implemented on Intel's Loihi neuromorphic research  ...  To our knowledge, this is the first work to show a Spiking Neural Network (SNN) implementation of the backpropagation algorithm that is fully on-chip, without a computer in the loop.  ...  In this study, we present a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing, implemented on Intel's Loihi neuromorphic research  ... 
arXiv:2106.07030v2 fatcat:3alzzvn3hngcpkrujsfdsyfukm

EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations [article]

Erwann Martin, Maxence Ernoult, Jérémie Laydevant, Shuai Li, Damien Querlioz, Teodora Petrisor, Julie Grollier
2021 arXiv   pre-print
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.  ...  Equilibrium Propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks.  ...  Data and code availability The data generated by this study is available on reasonable request.  ... 
arXiv:2010.07859v3 fatcat:w5fb6xbnfzggbarfmgxj4we4bi

A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

Duy-Anh Nguyen, Xuan-Tu Tran, Francesca Iacopi
2021 Journal of Low Power Electronics and Applications  
For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community.  ...  In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jlpea11020023 fatcat:rwhigu6tajeynabghkvszi5xa4

EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations

Erwann Martin, Maxence Ernoult, Jérémie Laydevant, Shuai Li, Damien Querlioz, Teodora Petrisor, Julie Grollier
2021 iScience  
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.  ...  Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks.  ...  on the error backpropagation (Falez et al., 2019) .  ... 
doi:10.1016/j.isci.2021.102222 pmid:33748709 pmcid:PMC7970361 fatcat:nuzf2fosi5alrfatno6fpk6qbu

Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning

Lei Deng, Huajin Tang, Kaushik Roy
2021 Frontiers in Computational Neuroscience  
AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ...  To alleviate the high cost training of SNNs using backpropagation, Lee et al. propose a spike-train level direct feedback alignment (ST-DFA) algorithm.  ...  They implement a surrogate gradient supervised learning algorithm on their efficient SNN platform, which further accounts for the impacts of device variation and limited bit precision of on-chip synaptic  ... 
doi:10.3389/fncom.2021.665662 pmid:33815083 pmcid:PMC8010134 fatcat:l5frrkuzprbovpb4tf327mhmtq

Neko: a Library for Exploring Neuromorphic Learning Rules [article]

Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia
2021 arXiv   pre-print
The field of neuromorphic computing is in a period of active exploration.  ...  Our results show that Neko can replicate the state-of-the-art algorithms and, in one case, lead to significant outperformance in accuracy and speed.  ...  This work is partially supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S.  ... 
arXiv:2105.00324v2 fatcat:5ddxkdcwtzf43mx5hkr3s52vaa

Spiking Neural Networks Hardware Implementations and Challenges

Maxence Bouvier, Alexandre Valentian, Thomas Mesquida, Francois Rummens, Marina Reyboz, Elisa Vianello, Edith Beigne
2019 ACM Journal on Emerging Technologies in Computing Systems  
In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms.  ...  We describe the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level and discuss their related advantages and challenges.  ...  algorithms on dedicated hardware.  ... 
doi:10.1145/3304103 fatcat:p3frra3osnhybj4hkor4y5cyqm

Convolutional networks for fast, energy-efficient neuromorphic computing

Steven K. Esser, Paul A. Merolla, John V. Arthur, Andrew S. Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J. Berg, Jeffrey L. McKinstry, Timothy Melano, Davis R. Barch, Carmelo di Nolfo, Pallab Datta (+4 others)
2016 Proceedings of the National Academy of Sciences of the United States of America  
For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step  ...  Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication  ...  We acknowledge Rodrigo Alvarez-Icaza for support with hardware infrastructure. This research was sponsored by the Defense Advanced Research Projects Agency under Contract FA9453-15-C-0055.  ... 
doi:10.1073/pnas.1604850113 pmid:27651489 pmcid:PMC5068316 fatcat:vlpd2cv725c2lfca4rwz3jkgvq

Neuromorphic Deep Learning Machines [article]

Emre Neftci, Charles Augustine, Somnath Paul, Georgios Detorakis
2017 arXiv   pre-print
neuromorphic hardware.  ...  operations that are difficult to realize in neuromorphic hardware.  ...  Acknowledgments This work was partly supported by the Intel Corporation and by the National Science Foundation under grant 1640081, and the Nanoelectronics Research Corporation (NERC), a wholly-owned subsidiary  ... 
arXiv:1612.05596v2 fatcat:mumivyfpxbfurg6xjsmq563pxa

Deep Learning With Spiking Neurons: Opportunities and Challenges

Michael Pfeiffer, Thomas Pfeil
2018 Frontiers in Neuroscience  
Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications.  ...  SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing.  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
doi:10.3389/fnins.2018.00774 pmid:30410432 pmcid:PMC6209684 fatcat:flcvj3c4tvfibhn2du3y6t3jvq

A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation [article]

Johannes C. Thiele, Olivier Bichler, Antoine Dupret, Sergio Solinas, Giacomo Indiveri
2019 arXiv   pre-print
The system is trained with a new version of the backpropagation algorithm adapted to on-chip learning in neuromorphic hardware: Error gradients are encoded as spike signals which are propagated through  ...  Our architecture is the first spiking neural network architecture with on-chip learning capabilities, which is able to perform relational inference on complex visual stimuli.  ...  ACKNOWLEDGMENT We would like to thank the CapoCaccia Workshop and the researchers at INI, with special attention to Matthew Cook, for the helpful discussions on the principles of the Network of Relations  ... 
arXiv:1903.04341v1 fatcat:dmi776ybvfdmdcrwfwdwgrtrfy

Event-driven random backpropagation: Enabling neuromorphic deep learning machines

Emre Neftci, Charles Augustine, Somnath Paul, Georgios Detorakis
2017 2017 IEEE International Symposium on Circuits and Systems (ISCAS)  
neuromorphic hardware.  ...  operations that are difficult to realize in neuromorphic hardware.  ...  Acknowledgments This work was partly supported by the Intel Corporation and by the National Science Foundation under grant 1640081, and the Nanoelectronics Research Corporation (NERC), a wholly-owned subsidiary  ... 
doi:10.1109/iscas.2017.8050529 dblp:conf/iscas/NeftciAPD17 fatcat:fqqfajaskrbhpgah7ogm2glvxi

Efficient Spike-Driven Learning With Dendritic Event-Based Processing

Shuangming Yang, Tian Gao, Jiang Wang, Bin Deng, Benjamin Lansdell, Bernabe Linares-Barranco
2021 Frontiers in Neuroscience  
The presented optimization makes the proposed DEP algorithm very suitable for implementation in digital or mixed-signal neuromorphic hardware.  ...  Exactly implementing a solution like backpropagation involves weight sharing, which requires additional bandwidth and computations in a neuromorphic system.  ...  AUTHOR CONTRIBUTIONS SY, TG, and BL-B developed the theoretical approach for DEP algorithm with spiking neurons. TG implemented the source code.  ... 
doi:10.3389/fnins.2021.601109 pmid:33679295 pmcid:PMC7933681 fatcat:3jxr5bqfmzg3rc3sfmv3oergty

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

Mike Davies, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, Sumedh R. Risbud
2021 Proceedings of the IEEE  
the key features of spike-based neuromorphic hardware.  ...  harness the key features of spike-based neuromorphic hardware more directly.  ...  AI capabilities on neuromorphic hardware.  ... 
doi:10.1109/jproc.2021.3067593 fatcat:krqdmy3u6jdvfl7btjglek5ag4

Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [article]

Malu Zhang, Jiadong Wang, Burin Amornpaisannon, Zhixuan Zhang, VPK Miriyala, Ammar Belatreche, Hong Qu, Jibin Wu, Yansong Chua, Trevor E. Carlson, Haizhou Li
2020 arXiv   pre-print
neuromorphic hardware systems.  ...  Furthermore, by utilizing the trained model parameters obtained from the proposed STDBP learning algorithm, we demonstrate the ultra-low-power inference operations on a recently proposed neuromorphic inference  ...  neuromorphic implementation.  ... 
arXiv:2003.11837v2 fatcat:lke7bezyhzbmpewpaactmbfyhm
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