ArticleFrontiers in neuroscience2018
GPUs Outperform Current HPC and Neuromorphic Solutions in Terms of Speed and Energy When Simulating a Highly-Connected Cortical Model.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 94 citations in OpenAlex.
- Efficient event-based delay learning in spiking neural networks.Nature communications · 2025Article
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Memory-efficient neurons and synapses for spike-timing-dependent-plasticity in large-scale spiking networks.Frontiers in neuroscience · 2024Article
- Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.Frontiers in neuroinformatics · 2024Article
- Listen to the Brain-Auditory Sound Source Localization in Neuromorphic Computing Architectures.Sensors (Basel, Switzerland) · 2023Article
- Geosmin suppresses defensive behaviour and elicits unusual neural responses in honey bees.Scientific reports · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- A scalable implementation of the recursive least-squares algorithm for training spiking neural networks.Frontiers in neuroinformatics · 2023Article
- Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2.Frontiers in neuroscience · 2023Article
- neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time.Frontiers in computational neuroscience · 2023Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models.Frontiers in neuroinformatics · 2022Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
- A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.Frontiers in neuroinformatics · 2022Article
- Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster.Frontiers in neuroinformatics · 2022Article
- Simulating the Cortical Microcircuit Significantly Faster Than Real Time on the IBM INC-3000 Neural Supercomputer.Frontiers in neuroscience · 2021Article
- Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution.Frontiers in neuroscience · 2021Article
- Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics.Frontiers in neuroscience · 2021Article
- PyGeNN: A Python Library for GPU-Enhanced Neural Networks.Frontiers in neuroinformatics · 2021Article
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2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While neuromorphic systems may be the ultimate platform for deploying spiking neural networks (SNNs), their distributed nature and optimization for specific types of models makes them unwieldy tools for developing them. Instead, SNN models tend to be developed and simulated on computers or clusters of computers with standard von Neumann CPU architectures. Over the last decade, as well as becoming a common fixture in many workstations, NVIDIA GPU accelerators have entered the High Performance Computing field and are now used in 50 % of the Top 10 super computing sites worldwide. In this paper we use our GeNN code generator to re-implement two neo-cortex-inspired, circuit-scale, point neuron network models on GPU hardware. We verify the correctness of our GPU simulations against prior results obtained with NEST running on traditional HPC hardware and compare the performance with respect to speed and energy consumption against published data from CPU-based HPC and neuromorphic hardware. A full-scale model of a cortical column can be simulated at speeds approaching 0.5× real-time using a single NVIDIA Tesla V100 accelerator-faster than is currently possible using a CPU based cluster or the SpiNNaker neuromorphic system. In addition, we find that, across a range of GPU systems, the energy to solution as well as the energy per synaptic event of the microcircuit simulation is as much as 14× lower than either on SpiNNaker or in CPU-based simulations. Besides performance in terms of speed and energy consumption of the simulation, efficient initialization of models is also a crucial concern, particularly in a research context where repeated runs and parameter-space exploration are required. Therefore, we also introduce in this paper some of the novel parallel initialization methods implemented in the latest version of GeNN and demonstrate how they can enable further speed and energy advantages.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.