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Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware
2020
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In this follow-on study, we present a modified architecture that includes several new mechanisms that enable implementation of the backpropagation algorithm using neuromorphic spiking units. ...
Neuromorphic systems have relied to date on conventional off-chip learning, and used on-chip computing only for inference [9, 10] . ...
doi:10.5167/uzh-198846
fatcat:nrn6aojyg5gjfmxkvkfynrvnb4
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
[article]
2021
arXiv
pre-print
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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 ...
In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware. ...
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]
2021
arXiv
pre-print
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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
Deep Learning With Spiking Neurons: Opportunities and Challenges
2018
Frontiers in Neuroscience
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deep learning, but simultaneously allows for efficient mapping to hardware. ...
This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. ...
ACKNOWLEDGMENTS We would like to thank David Stöckel, Volker Fischer, and Andre Guntoro for critical reading and helpful discussions. ...
doi:10.3389/fnins.2018.00774
pmid:30410432
pmcid:PMC6209684
fatcat:flcvj3c4tvfibhn2du3y6t3jvq
A Review of Algorithms and Hardware Implementations for Spiking Neural Networks
2021
Journal of Low Power Electronics and Applications
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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. ...
Figure 5 5 shows a general architecture for large-scale, scalable neuromorphic hardware.
Figure 5 . 5 General strategy for the large-scale implementation of neuromorphic hardware. ...
doi:10.3390/jlpea11020023
fatcat:rwhigu6tajeynabghkvszi5xa4
Neuromorphic Deep Learning Machines
[article]
2017
arXiv
pre-print
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Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations in neuromorphic computing hardware. ...
neuromorphic hardware. ...
We thank Jun-Haeng Lee and Peter O'Connor for review and comments; and Gert Cauwenberghs, João Sacramento, Walter Senn for discussion. ...
arXiv:1612.05596v2
fatcat:mumivyfpxbfurg6xjsmq563pxa
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
2021
iScience
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, and comparing favorably to alternative learning techniques for spiking neural networks. ...
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. ...
EqSpike with its local two-factor online learning rule therefore reaches accuracies on MNIST comparable to those of the latest models investigated for training spiking neural networks on hardware platforms ...
doi:10.1016/j.isci.2021.102222
pmid:33748709
pmcid:PMC7970361
fatcat:nuzf2fosi5alrfatno6fpk6qbu
Event-driven random backpropagation: Enabling neuromorphic deep learning machines
2017
2017 IEEE International Symposium on Circuits and Systems (ISCAS)
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Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations in neuromorphic computing hardware. ...
neuromorphic hardware. ...
We thank Jun-Haeng Lee and Peter O'Connor for review and comments; and Gert Cauwenberghs, João Sacramento, Walter Senn for discussion. ...
doi:10.1109/iscas.2017.8050529
dblp:conf/iscas/NeftciAPD17
fatcat:fqqfajaskrbhpgah7ogm2glvxi
Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning
2021
Frontiers in Computational Neuroscience
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AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. ...
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 ...
Compared to the state-of-the-art backpropagation learning algorithm, they demonstrate excellent performance vs. overhead tradeoffs on FPGA for speech and image classification applications. ...
doi:10.3389/fncom.2021.665662
pmid:33815083
pmcid:PMC8010134
fatcat:l5frrkuzprbovpb4tf327mhmtq
Neko: a Library for Exploring Neuromorphic Learning Rules
[article]
2021
arXiv
pre-print
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While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking models, general software libraries for learning rules remain underexplored. ...
brain to constrained learning algorithms deployed on memristor crossbars. ...
ACKNOWLEDGMENTS We thank Sihong Wang and Shilei Dai for helpful discussions. ...
arXiv:2105.00324v2
fatcat:5ddxkdcwtzf43mx5hkr3s52vaa
Convolutional networks for fast, energy-efficient neuromorphic computing
2016
Proceedings of the National Academy of Sciences of the United States of America
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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
Spiking Neural Networks Hardware Implementations and Challenges
2019
ACM Journal on Emerging Technologies in Computing Systems
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2020 pre-print arXiv:2005.01467v1 fatcat:i7mtmpuxfnbmzpy367z7ys4xrm
Neuromorphic computing is henceforth a major research field for both academic and industrial actors. ...
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. ...
Machine learning may rely on simplified spiking units, such as LIF or IF models, to enable accurate and efficient hardware implementations. ...
doi:10.1145/3304103
fatcat:p3frra3osnhybj4hkor4y5cyqm
Online Few-shot Gesture Learning on a Neuromorphic Processor
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2008.01151v1 fatcat:6c3iz4jdmvbjbari3b55xvjqma
We show that partially trained deep Spiking Neural Networks (SNNs) implemented on neuromorphic hardware can rapidly adapt online to new classes of data within a domain. ...
We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learning on neuromorphic processors. ...
ACKNOWLEDGEMENTS The preliminary experiments of this research were conducted at the Telluride Neuromorphic Cognition Engineering workshop, years 2018 and 2019 (all authors). This research ...
arXiv:2008.01151v2
fatcat:ue36farxabcq5gu3aqvvrb5c3q
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
[article]
2019
arXiv
pre-print
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Here, we demonstrate on-line surrogate gradient few-shot learning on Intel's Loihi neuromorphic research processor using features pre-trained with spike-based gradient backpropagation-through-time. ...
This is incompatible with learning systems that do not have boundaries between training and inference, such as in neuromorphic hardware. ...
As the plasticity rule is improved, the applicability is likely to extend beyond classification, namely unsupervised learning and reinforcement learning tasks.
V. ACKNOWLEDGMENTS ...
arXiv:1910.04972v6
fatcat:z53pn5mpujaoxmx52qvn6oluyq
A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation
[article]
2019
arXiv
pre-print
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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 ...
These features make our system interesting for sensor fusion applications and embedded learning in autonomous neuromorphic agents. ...
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
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