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Substantial evidence indicates that the time structure of neuronal spike trains is relevant in neuronal signal processing. Bio-inspired spiking neural networks are taking these results into account. Applications of these networks to low vision problems, e.g. segmentation, requires that the simulation of large-scale networks must be performed in a reasonable(More)
We present the basic architecture of a Memory Optimized Accelerator for Spiking Neural Networks (MASPINN). The accelerator architecture exploits two novel concepts for an efficient computation of spiking neural networks: weight caching and a compressed memory organization. These concepts allow a further parallelization in processing and reduce bandwidth(More)
In this paper, we present a digital system called (SP/sup 2/INN) for simulating very large-scale spiking neural networks (VLSNNs) comprising, e.g., 1000000 neurons with several million connections in total. SP/sup 2/INN makes it possible to simulate VLSNN with features such as synaptic short term plasticity, long term plasticity as well as configurable(More)
Computing complex spiking artificial neural networks (SANNs) on conventional hardware platforms is far from reaching real-time requirements. Therefore we propose a neuro-processor, called NeuroPipe-Chip, as part of an accelerator board. In this paper, we introduce two new concepts on chip-level to speed up the computation of SANNs. These concepts are(More)
The fast simulation of large networks of spiking neurons is a major task for the examination of biology-inspired vision systems. Networks of this type label features by synchronization of spikes and there is strong demand to simulate these eeects in real world environments. As the calculations for one model neuron are complex, the digital simulation of(More)
Conventional hardware platforms are far from reaching real-time simulation requirements of complex spiking neural networks (SNN). Therefore we designed an accelerator board with a neuro-processor-chip, called NeuroPipe-Chip. In this paper, we introduce two new concepts on chip-level to speed up the simulation of SNN. The concepts are implemented in the(More)
The simulation of large spiking neural networks (PCNN) especially for vision purposes is limited by the computing power of general purpose computer systems [5,9,10]. Therefore, the simulation of real world scenarios requires dedicated simulator systems. This article presents architec-tures of software and hardware implementations for PCNN simulator systems.(More)