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DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware [article]

Shihao Song, Harry Chong, Adarsha Balaji, Anup Das, James Shackleford, Nagarajan Kandasamy
2021 arXiv   pre-print
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]

Adarsha Balaji and Thibaut Marty and Anup Das and Francky Catthoor
2020 arXiv   pre-print
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]

Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya, Aron Leibfried, Andreas Grübl, Vitali Karasenko, Christian Pehle, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Johannes Schemmel, Friedemann Zenke
2021 arXiv   pre-print
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

Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya, Aron Leibfried, Andreas Grübl, Vitali Karasenko, Christian Pehle, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Johannes Schemmel, Friedemann Zenke
2022 Proceedings of the National Academy of Sciences of the United States of America  
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

Maxence Bouvier, Alexandre Valentian, Thomas Mesquida, Francois Rummens, Marina Reyboz, Elisa Vianello, Edith Beigne
2019 ACM Journal on Emerging Technologies in Computing Systems  
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

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.  ...  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

Michael Pfeiffer, Thomas Pfeil
2018 Frontiers in Neuroscience  
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

Kristofor D. Carlson, Jayram Moorkanikara Nageswaran, Nikil Dutt, Jeffrey L. Krichmar
2014 Frontiers in Neuroscience  
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

Chenglong Zou, Xiaoxin Cui, Yisong Kuang, Kefei Liu, Yuan Wang, Xinan Wang, Ru Huang
2021 Frontiers in Neuroscience  
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]

Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Jing Pei, Youhui Zhang (+2 others)
2021 arXiv   pre-print
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]

Peter U. Diehl, Bruno U. Pedroni, Andrew Cassidy, Paul Merolla, Emre Neftci, Guido Zarrella
2016 arXiv   pre-print
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]

Shail Dave, Alberto Marchisio, Muhammad Abdullah Hanif, Amira Guesmi, Aviral Shrivastava, Ihsen Alouani, Muhammad Shafique
2022 arXiv   pre-print
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

Peter U. Diehl, Bruno U. Pedroni, Andrew Cassidy, Paul Merolla, Emre Neftci, Guido Zarrella
2016 2016 International Joint Conference on Neural Networks (IJCNN)  
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

Evelina Forno, Alessandro Salvato, Enrico Macii, Gianvito Urgese
2021 Journal of Low Power Electronics and Applications  
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]

Joshua Mack, Ruben Purdy, Kris Rockowitz, Michael Inouye, Edward Richter, Spencer Valancius, Nirmal Kumbhare, Md Sahil Hassan, Kaitlin Fair, John Mixter, Ali Akoglu
2020 arXiv   pre-print
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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