FPGA Accelerated Simulation of Biologically Plausible Spiking Neural Networks

Artificial neural networks are a key tool for researchers attempting to understand and replicate the behaviour and intelligence found in biological neural networks. Software simulations offer great flexibility and the ability to select which aspects of biological networks to model, but are slow when operating on more complex biologically plausible models; while dedicated hardware solutions can be very fast, they are restricted to fixed models. This paper uses FPGAs to achieve a compromise between model complexity and simulation speed, such that a fully-connected network of 1024 neurons,based on the biologically plausible Izhikevich spiking model,can be simulated at 100 times real-time speed. The simulator is based on a re-usable interconnection architecture for storing synapse weights and calculating thalamic input, which makes use of the large number of available block-RAMs and huge amounts of fine-grain parallelism. The simulator achieves a sustained throughput of 2.26 GFlops in double-precision, and a single Virtex-5 xc5vlx330t without off-chip storage running at 133 MHz is 16 times faster than a 3 GHz Core2 CPU, and 1.1 times faster than a single-precision 1.2 GHz 30-core GPU.