NeuroFlow: FPGA-based Spiking Neural Network Acceleration with High-level Language Support
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1
Imperial College London, Department of Computing, United Kingdom
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2
Imperial College London, Department of Bioengineering, United Kingdom
Spiking neural networks are a useful tool for cortical modelling and robotic control, but simulating a large network in real-time requires high-performance computers or specially built accelerators. Traditional accelerators for large-scale spiking neural network accelerators developed previously use Graphics Processing Units (GPUs) or Application-Specific Integrated Circuit (ASIC) chips. While ASICs deliver high performance, they lack the flexibility to reconfigure and hence are unable to adapt variation in the design and models employed. On the other hand, GPUs provides a decent speedup over multi-core CPUs and good flexibility, but it lacks scalability to handle larger networks.
In this work we present NeuroFlow, a Field Programmable Gate Array (FPGA)-based spiking neural network accelerator consisting of 4 FPGAs. It supports the use of PyNN, a high-level simulator-independent network description language, to configure the hardware. A major novelty of the system is the capability to provide custom hardware configuration based on various simulation requirements, such as precision and time delay. The accelerator is implemented on an off-the-shelf MPC-C500 from Maxeler Technology which employs a streaming architecture in the FPGAs. The accelerator achieves the performance gain primarily by parallelizing the computation of point-neuron models and employing low-level optimization for synaptic data memory access. The accelerator currently supports basic PyNN functions such as spike-timing-dependent plasticity (STDP) and arbitrary postsynaptic current kernels.
The system is able to support simulation of network of approximately 800,000 neurons, and achieve a real-time performance of 400,000 neurons for a network firing at 8Hz with random connections. With a single FPGA running at 150MHz, the accelerator delivers a throughput of 1.9 times to 3.5 times the performance of one of the most recent GPU-based accelerators in terms of postsynaptic potential delivery rate (Fidjeland et al., Neuroinformatics, 2012 Dec), subject to the simulated network and the GPU model used.
In conclusion, while harnessing low-level customization and fine grained parallelism in FPGA, NeuroFlow is also able to provide the flexibility of a high-level platform such as GPUs and high-performance computers. It provides a promising alternative to accelerate spiking neural network simulations.
Acknowledgements
The research leading to these results has received funding from European Union Seventh Framework Programme under grant agreement number 287804, 248976 and 257906. The support by the Croucher Foundation, UK EPSRC, HiPEAC NoE, Maxeler University Program, and Xilinx is gratefully acknowledged.
Keywords:
FPGA,
Spiking Neural network,
PyNN,
Hardware Acceleration,
Dataflow Computing
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Oral presentation
Topic:
Neuromorphic engineering
Citation:
Cheung
K,
Schultz
SR and
Luk
W
(2013). NeuroFlow: FPGA-based Spiking Neural Network Acceleration with High-level Language Support.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00095
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Received:
08 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Mr. Kit Cheung, Imperial College London, Department of Computing, London, United Kingdom, k.cheung11@imperial.ac.uk