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Reconfigurable platforms and the challenges for large-scale implementations of spiking neural networks

Jim Harkin, Fearghal Morgan, Steve Hall, Piotr Dudek, Thomas Dowrick, Liam McDaid
2008 2008 International Conference on Field Programmable Logic and Applications  
FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconfigurability.  ...  The paper presents a novel Field Programmable Neural Network (FPNN) architecture incorporating low power analogue synapse and a network on chip architecture for SNN routing and configuration.  ...  The aim of this strategy is to use the router of each tile to communicate spike events to and from its group of n synapses.  ... 
doi:10.1109/fpl.2008.4629989 dblp:conf/fpl/HarkinMHDDM08 fatcat:eevaoiwaafc2dj7h2p3jt7gtya

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks [article]

Adarsha Balaji and Shihao Song and Twisha Titirsha and Anup Das and Jeffrey Krichmar and Nikil Dutt and James Shackleford and Nagarajan Kandasamy and Francky Catthoor
2021 arXiv   pre-print
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN).  ...  NeuroXplorer's optimization engine can incorporate hardware-oriented metrics such as energy, throughput, and latency, as well as SNN-oriented metrics such as inter-spike interval distortion and spike disorder  ...  NeuroXplorer can work with both Artificial Neural Networks (ANNs) and biology-inspired Spiking Neural Networks (SNNs).  ... 
arXiv:2105.01795v1 fatcat:yztiegjepvho5ecztv2akaj4vy

On-chip communication for neuro-glia networks

George Martin, Jim Harkin, Liam J. McDaid, John J. Wade, Junxiu Liu
2018 IET Computers & Digital Techniques  
This supports local communication for self-repair. The networks-on-chip (NoC) was implemented for local communication between the astrocytes and neurons.  ...  Therefore, mapping neuro-glia networks to hardware provides a strategy for achieving self-repair in hardware. The internal interconnecting of these networks in hardware is a challenge.  ...  The astrocyte network can be viewed as a high-level network, working in parallel with the neural network, responsible for regulating synaptic plasticity through neural networks.  ... 
doi:10.1049/iet-cdt.2017.0187 fatcat:xjx44h24inc3nhkg64r2guert4

Self-organization of multi-layer spiking neural networks [article]

Guruprasad Raghavan, Cong Lin, Matt Thomson
2020 arXiv   pre-print
Inspired by this strategy, we attempt to efficiently self-organize large neural networks with an arbitrary number of layers into a wide variety of architectures.  ...  The dynamical system encapsulates the dynamics of spiking units, their inter/intra layer interactions as well as the plasticity rules that control the flow of information between layers.  ...  In this paper, we develop strategies to self-organize large spatially-connected, multi-layer spiking neural networks (SNN), inspired by the wiring rules and mechanisms adopted by the mammalian visual system  ... 
arXiv:2006.06902v1 fatcat:3527vglixfbxnjv3odmklh7mle

What does gamma coherence tell us about inter-regional neural communication?

György Buzsáki, Erik W Schomburg
2015 Nature Neuroscience  
Here we discuss how measurements of inter-regional gamma coherence can be prone to misinterpretation and suggest strategies for deciphering the roles that synchronized oscillations across brain networks  ...  communication.  ...  One possibility for observing neural transmission from network A to network B is to record the output spikes in the somatic layer of network A (electrode A SOMA ) and monitor the transmembrane currents  ... 
doi:10.1038/nn.3952 pmid:25706474 pmcid:PMC4803441 fatcat:6dgoxha35bbxto2t5qu5euh4je

Distributed simulation of polychronous and plastic spiking neural networks: strong and weak scaling of a representative mini-application benchmark executed on a small-scale commodity cluster [article]

Pier Stanislao Paolucci, Roberto Ammendola, Andrea Biagioni, Ottorino Frezza, Francesca Lo Cicero, Alessandro Lonardo, Elena Pastorelli, Francesco Simula, Laura Tosoratto, Piero Vicini
2014 arXiv   pre-print
Here, we present the strong and weak scaling and the profiling of the computational/communication components of the DPSNN-STDP benchmark (Distributed Simulation of Polychronous Spiking Neural Network with  ...  We introduce a natively distributed mini-application benchmark representative of plastic spiking neural network simulators.  ...  : inter-process multicast: Spikes dim (0.77±0.10)% Message passing Communication: inter-process multicast: Spikes payload (0.82±0.20)% Message passing Axonal to synaptic spikes: intra-process multicast  ... 
arXiv:1310.8478v2 fatcat:6wac3pcpzvdjhawi7rtrtlnzwu

An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

Runchun Wang, Gregory Cohen, Klaus M. Stiefel, Tara Julia Hamilton, Jonathan Tapson, André van Schaik
2013 Frontiers in Neuroscience  
We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns.  ...  Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns.  ...  This is a feature typical of a polychronous spiking neural network. The more clock cycles a spike lasts, the more robust the communication will be.  ... 
doi:10.3389/fnins.2013.00014 pmid:23408739 pmcid:PMC3570898 fatcat:25x2rbkrm5fgtpyithgr4vgk2a

Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections [article]

Elena Pastorelli and Cristiano Capone and Francesco Simula and Maria V. Sanchez-Vives and Paolo Del Giudice and Maurizio Mattia and Pier Stanislao Paolucci
2019 arXiv   pre-print
Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which  ...  We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying  ...  Neural Activity Analysis The simulation generates the spikes produced by each neuron in the network.  ... 
arXiv:1902.08410v1 fatcat:k2ekn5k4o5hwth7tp6z3545s6y

Hardware event-driven simulation engine for spiking neural networks

R. Agís, E. Ros, J. Díaz, R. Carrillo, E. M. Ortigosa
2007 International journal of electronics (Print)  
We have adopted a strategy that handles efficiently disordered event lists, which is a completely novel approach in the framework of event-driven spiking neural network simulation.  ...  Here, we need to handle inter-spike risks.  ... 
doi:10.1080/00207210701308625 fatcat:wwuxn25ij5cona2dgulfqwcqga

Scaling of a Large-Scale Simulation of Synchronous Slow-Wave and Asynchronous Awake-Like Activity of a Cortical Model With Long-Range Interconnections

Elena Pastorelli, Cristiano Capone, Francesco Simula, Maria V. Sanchez-Vives, Paolo Del Giudice, Maurizio Mattia, Pier Stanislao Paolucci
2019 Frontiers in Systems Neuroscience  
Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which  ...  We explored networks up to 192 × 192 modules, each composed of 1,250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying  ...  For instance, we expect that the delivery of spiking messages will be a key element to be further optimized (e.g., using a hierarchical communication strategy).  ... 
doi:10.3389/fnsys.2019.00033 pmid:31396058 pmcid:PMC6664086 fatcat:nqhg25ez5zc2tl4nb524jf5l5y

Gaussian and Exponential Lateral Connectivity on Distributed Spiking Neural Network Simulation

Elena Pastorelli, Pier Stanislao Paolucci, Francesco Simula, Andrea Biagioni, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Alessandro Lonardo, Michele Martinelli, Luca Pontisso, Piero Vicini (+1 others)
2018 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)  
We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range  ...  Two-dimensional grids of cortical columns composed by up to 11 M point-like spiking neurons with spike frequency adaption were connected by up to 30 G synapses using short- and long-range connectivity  ...  Orange blocks are used for the inter-processes communication tasks.  ... 
doi:10.1109/pdp2018.2018.00110 dblp:conf/pdp/PastorelliPSBCC18 fatcat:3gory6grabcodktarzoxko7ap4

SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor

M.M. Khan, D.R. Lester, L.A. Plana, A. Rast, X. Jin, E. Painkras, S.B. Furber
2008 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)  
SpiNNaker is a novel chip -based on the ARM processor -which is designed to support large scale spiking neural networks simulations.  ...  Our eventual goal is to be able to simulate neural networks consisting of 10 9 neurons running in 'real time', by which we mean that a similarly sized collection of biological neurons would run at the  ...  If a system for spiking neural networks is to be biologically plausible, therefore, it must display at least reasonable scalability, so that the same mapping and routing strategies that are effective for  ... 
doi:10.1109/ijcnn.2008.4634199 dblp:conf/ijcnn/KhanLPRJPF08 fatcat:w6jacfvycrajxead7t5vedmjue

Mapping of local and global synapses on spiking neuromorphic hardware

Anup Das, Yuefeng Wu, Khanh Huynh, Francesco Dell'Anna, Francky Catthoor, Siebren Schaafsma
2018 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)  
Our framework is implemented in Python, interfacing CARLsim, a GPU-accelerated application-level spiking neural network simulator with an extended version of Noxim, for simulating time-multiplexed interconnects  ...  Spiking Neural Networks (SNNs) are widely deployed to solve complex pattern recognition, function approximation and image classification tasks.  ...  First, PACMAN is primarily targeted for architectures supported on SpiNNaker such as Deep Belief Networks [9] and Convolution Neural Networks [10] .  ... 
doi:10.23919/date.2018.8342201 dblp:conf/date/0001WHDCS18 fatcat:66xqcf5qtng7dckw4tgpr2rwmy

Parkinson's Disease Classification using Various Advanced Neural Network Classifiers

2019 International journal of recent technology and engineering  
Online Meta-neuron based Learning Algorithm (OMLA) is a newly evolved network applied for Parkinson's disease classification.  ...  Network (PBL-McRBFN) for Parkinson's disease classification.  ...  Online Meta-neuron based Learning Algorithm (OMLA) is a special class of artificial neural network (ANN), where neuron models communicate with spikes.  ... 
doi:10.35940/ijrte.d7924.118419 fatcat:hxfecatwsjemrbgmcyavt3hsk4

Simulation Tool for Asynchronous Cortical Streams (STACS): Interfacing with Spiking Neural Networks

Felix Wang
2015 Procedia Computer Science  
We present a Simulation Tool for Asynchronous Cortical Streams (STACS) for studying spiking neural networks exhibiting adaptation in a closed-loop system.  ...  In particular, STACS facilitates the development of network level metrics of spiking activity.  ...  Event-based communication Much of the computation that occurs in a spiking neural network rests on top of events, namely the spiking of a neuron.  ... 
doi:10.1016/j.procs.2015.09.149 fatcat:kps6x4rp6ff47h5ftrbfqbfatq
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