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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...
This demonstration shows the first implementation of a real-time spike-based convolution processing system which combines a spike based dynamic vision sensor (DVS) with parallel graphics processor unit (GPU) computation. Moving objects with different features (shape and size) are presented to the system. In the first demo, the system responses in real time to recognize and keep track of one user specified...
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...
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