ArticleFrontiers in neuroscience2015
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network.Frontiers in neuroscience · 2023Article
- A System-on-Chip Based Hybrid Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks.Frontiers in neuroinformatics · 2022Article
- Probabilistic Spike Propagation for Efficient Hardware Implementation of Spiking Neural Networks.Frontiers in neuroscience · 2021Article
- Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions.Neurocomputing · 2020Article
- Article
- Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain.Frontiers in neuroscience · 2018Review
- Code Generation in Computational Neuroscience: A Review of Tools and Techniques.Frontiers in neuroinformatics · 2018Review
- Computing Generalized Matrix Inverse on Spiking Neural Substrate.Frontiers in neuroscience · 2018Article
- An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator.Frontiers in neuroscience · 2018Article
- A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks.Frontiers in neurorobotics · 2018Review
- A Scalable FPGA Architecture for Randomly Connected Networks of Hodgkin-Huxley Neurons.Frontiers in neuroscience · 2018Article
- An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks.Frontiers in neuroscience · 2017Article
- Connecting the Brain to Itself through an Emulation.Frontiers in neuroscience · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.
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Registered trials
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.