Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors

IJCNN (2009)

Jayram Moorkanikara Nageswaran, Nikil Dutt, Jeffrey L Krichmar, Alex Nicolau, Alex V

Abstract. Abstract—Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Graphics Processing Units (GPUs) can provide a low-cost, programmable, and highperformance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, Izhikevich neuron based large-scale SNN simulator that runs on a single GPU. The GPU-SNN model (running on an NVIDIA GTX-280 with 1GB of memory), is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7Hz. For simulation of 100K neurons with 10 Million synaptic connections, the GPUSNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and compact network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results were validated against CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. We intend to make our simulator available to the modeling community so that researchers will have easy access to large-scale SNN simulations.

Software Developers: Jayram Moorkanikara Nageswaran, Micah Richert, Michael Wei

Full text in PDF file,   Source Code (Version 0.2) Computing Spike Based Convolutions on GPUs

ISCAS (2008)

Jayram Moorkanikara Nageswaran, Yingxue Wang, Tobi Delbruck, Nikil Dutt

Abstract. In spiking neural networks, asynchronous spike events are processed in parallel by neurons. Emulations of such networks are traditionally computed by CPUs or realized using dedicated neuromorphic hardware. In many neuromorphic systems, the Address-Event-Representation (AER) is used for spike communication. In this paper we present the acceleration of AER based spike processing using a Graphics Processing Unit (GPU). In our experiment we interface a 128x128 pixel AER vision sensor to a spiking neural network implemented on a GPU for real-time convolution-based nonlinear feature extraction with convolution kernel sizes ranging from 48x48 to 112x112 pixels. We show parallelism-performance trade-offs on GPUs for single spike per thread, multiple spikes per thread, and multiple objects parallelism techniques. Our implementation can achieve a kernel speedup of up to 35x on a single NVIDIA GTX280 board when compared to a CPU-only implementation.

Full text in PDF file,   Source Code (to be provided)

[home] [research] [publications] [cv]