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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 ...
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]
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
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. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jlpea11020023
fatcat:rwhigu6tajeynabghkvszi5xa4
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
2021
iScience
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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. ...
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
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. ...
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]
2021
arXiv
pre-print
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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
2019
ACM Journal on Emerging Technologies in Computing Systems
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2020 pre-print arXiv:2005.01467v1 fatcat:i7mtmpuxfnbmzpy367z7ys4xrm
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
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
Neuromorphic Deep Learning Machines
[article]
2017
arXiv
pre-print
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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
2018
Frontiers in Neuroscience
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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]
2019
arXiv
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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 ...
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
2017
2017 IEEE International Symposium on Circuits and Systems (ISCAS)
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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
2021
Frontiers in Neuroscience
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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
2021
Proceedings of the IEEE
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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]
2020
arXiv
pre-print
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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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