Title |
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
|
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Published in |
Frontiers in Neuroscience, January 2016
|
DOI | 10.3389/fnins.2015.00516 |
Pubmed ID | |
Authors |
Kit Cheung, Simon R. Schultz, Wayne Luk |
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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Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 25% |
United Kingdom | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 1% |
Brazil | 1 | <1% |
Germany | 1 | <1% |
Finland | 1 | <1% |
Croatia | 1 | <1% |
Unknown | 133 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 37 | 27% |
Student > Master | 25 | 18% |
Researcher | 18 | 13% |
Student > Bachelor | 9 | 6% |
Professor > Associate Professor | 6 | 4% |
Other | 23 | 17% |
Unknown | 21 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 55 | 40% |
Computer Science | 32 | 23% |
Neuroscience | 12 | 9% |
Agricultural and Biological Sciences | 8 | 6% |
Physics and Astronomy | 3 | 2% |
Other | 7 | 5% |
Unknown | 22 | 16% |