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DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware
[article]
2021
arXiv
pre-print
Preserved Fulltext
Third, it exploits the rich semantics of Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of ...
We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. ...
Using these constraints and the new formulation, one can estimate the throughput of a clustered SNN on a neuromorphic hardware for a specific actor-to-tile mapping. ...
arXiv:2108.02023v1
fatcat:5yuttyxivnhb7klhhv7blniwhm
Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
[article]
2020
arXiv
pre-print
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Our design methodology operates in two steps – step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture ...
In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at run-time. ...
The framework allows users to analyze and optimize the partitioning and mapping of an SNN on cycle-accurate models of neuromorphic hardware. ...
arXiv:2006.06777v1
fatcat:bwncem4t55fq3eugwzg2mxawz4
Surrogate gradients for analog neuromorphic computing
[article]
2021
arXiv
pre-print
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In summary, our work sets several new benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms. ...
Here, we introduce a general in-the-loop learning framework based on surrogate gradients that resolves these issues. ...
Spilger for their work on the software environment, B. Kindler, F. Kleveta, and S. Schmitt for their helpful support, A. ...
arXiv:2006.07239v3
fatcat:5n4kkttobrfjfkmydjgtrmwila
Surrogate gradients for analog neuromorphic computing
2022
Proceedings of the National Academy of Sciences of the United States of America
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In summary, our work sets several benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms. ...
However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. ...
Spilger for their work on the software environment; B. Kindler, F. Kleveta, and S. Schmitt for their helpful support; A. ...
doi:10.1073/pnas.2109194119
pmid:35042792
pmcid:PMC8794842
fatcat:j3x7ocfndjanrhxys6rbicqrgi
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
The scope of existing solutions is extensive; we thus present the general framework and study on a case-by-case basis the relevant particularities. ...
Neuromorphic computing is henceforth a major research field for both academic and industrial actors. ...
Algorithm to Hardware Mapping. The mapping strategy employed to evaluate a SNN onto a neuromorphic hardware may largely affect the performances of the hardware evaluation of the circuit. ...
doi:10.1145/3304103
fatcat:p3frra3osnhybj4hkor4y5cyqm
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. ...
Deep learning offers a fine starting point in a journey of SNN algorithm discovery, but it represents only one niche of the algorithmic universe available to neuromorphic hardware. ...
doi:10.1109/jproc.2021.3067593
fatcat:krqdmy3u6jdvfl7btjglek5ag4
Deep Learning With Spiking Neurons: Opportunities and Challenges
2018
Frontiers in Neuroscience
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SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing. ...
A wide range of training methods for SNNs is presented, ranging from the conversion of conventional deep networks into SNNs, constrained training before conversion, spiking variants of backpropagation, ...
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
An efficient automated parameter tuning framework for spiking neural networks
2014
Frontiers in Neuroscience
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Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. ...
A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation ...
The automated parameter tuning framework is publicly available and could be very useful for the implementation of large-scale SNNs on neuromorphic hardware or for the development of large-scale SNN simulations ...
doi:10.3389/fnins.2014.00010
pmid:24550771
pmcid:PMC3912986
fatcat:knmr4rftn5bcdo6hehlui3mq5y
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware
2021
Frontiers in Neuroscience
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Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. ...
Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. ...
Besides, we develop an incremental and resourceefficient mapping framework for these SNNs on a reconfigurable neuromorphic ASIC. ...
doi:10.3389/fnins.2021.694170
pmid:34867142
pmcid:PMC8636746
fatcat:mipdo4exlvfntjk6uhum2blvma
Brain-inspired global-local learning incorporated with neuromorphic computing
[article]
2021
arXiv
pre-print
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At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs for exploiting the advantages. ...
We further implemented the hybrid model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and proved that the model can fully utilize neuromorphic many-core architecture ...
Data and code availability All data used in this paper are publicly available and can be accessed at http://yann.lecun.com/exdb/mnist/ for the MNIST dataset, https://www.cs.toronto.edu/~kriz/cifar/ ...
arXiv:2006.03226v3
fatcat:rpx3rt56lzbzrhffdcipfxtuji
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth
[article]
2016
arXiv
pre-print
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Additionally, we show that the SNN generated with this technique can be mapped to existing neuromorphic hardware -- in our case, the TrueNorth chip. ...
Specifically, we perform a fine-grained sentiment analysis task with implementations on two different systems: one using conventional spiking neural network (SNN) simulators and the other one using IBM's ...
Acknowledgments We thank the organizers and the participants of Telluride Neuromorphic Cognition Engineering Workshop 2015 for the unique environment that enabled the presented work. ...
arXiv:1601.04183v1
fatcat:l2qlupx2ojbb7dywkasvl4w2zq
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems
[article]
2022
arXiv
pre-print
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and secure ML systems based on user-defined constraints and objectives. ...
The real-world use cases of Machine Learning (ML) have exploded over the past few years. ...
Security for SNN-based Neuromorphic Architectures: On neuromorphic architectures, the adversarial attacks and defenses can take advantage on different properties. ...
arXiv:2204.09514v1
fatcat:ho7auszvmferrn36evs7oqdpt4
TrueHappiness: Neuromorphic emotion recognition on TrueNorth
2016
2016 International Joint Conference on Neural Networks (IJCNN)
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Additionally, we show that the SNN generated with this technique can be mapped to existing neuromorphic hardware -in our case, the TrueNorth chip. ...
Specifically, we perform a fine-grained sentiment analysis task with implementations on two different systems: one using conventional spiking neural network (SNN) simulators and the other one using IBM's ...
Acknowledgments We thank the organizers and the participants of Telluride Neuromorphic Cognition Engineering Workshop 2015 for the unique environment that enabled the presented work. ...
doi:10.1109/ijcnn.2016.7727758
dblp:conf/ijcnn/DiehlPCMNZ16
fatcat:kgx2vmv4nbaw3agxbktwmwtgtq
PageRank Implemented with the MPI Paradigm Running on a Many-Core Neuromorphic Platform
2021
Journal of Low Power Electronics and Applications
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SpiNNaker is a neuromorphic hardware platform, especially designed for the simulation of Spiking Neural Networks (SNNs). ...
and speed limitations on the current version of the hardware. ...
Acknowledgments: We thank the Advanced Processor Technologies Group led by Steve Furber at University of Manchester for the technical support. ...
doi:10.3390/jlpea11020025
fatcat:4yvy7jddrngoje7vkxmr5sc6zy
RANC: Reconfigurable Architecture for Neuromorphic Computing
[article]
2020
arXiv
pre-print
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In order to lift the barriers to entry for hardware designers and application developers we present RANC: a Reconfigurable Architecture for Neuromorphic Computing, an open-source highly flexible ecosystem ...
We present post routing resource usage and throughput analysis across implementations of Synthetic Aperture Radar classification and Vector Matrix Multiplication applications, and demonstrate a neuromorphic ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of RMS. ...
arXiv:2011.00624v1
fatcat:blfn6a6s7bco7omnhx5tr4dnfu
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