Optimizing neural networks on SIMD parallel computers
Parallel Computing
Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Multifold Acceleration of Neural Network Computations Using GPU
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Cyfield-RISP: generating dynamic instruction set processors for reconfigurable hardware using OpenCL
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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The algorithms used for simulating biologically-inspired spiking neural networks (BIANN) often utilize functions which are computationally complex and have to model a large number of neurons - or even a much larger number of synapses in parallel. To use all available computing resources provided by a standard desktop PC is an opportunity to shorten the simulation time and extend the number of simulated neurons and their interconnections. OpenCL offers an open platform for heterogeneous computing to employ CPUs, GPUs, DSP or FPGAs in an uniform way. This paper introduces a handy simulation framework being sufficient to accelerate different kinds of neural networks with off-the-shelf hardware. To illustrate this, different large networks comprising a complex synaptic model in combination with a leaky Integrate-and-Fire neuron model are implemented as standard Matlab code and with OpenCL separately. In comparison to the Matlab model, OpenCL reaches a speedup of ~ 83 on a quad-core processor and of ∼ 1500 on a GPU.