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 implemented in a prototype of the NeuroPipe-Chip. We present the hardware structure of the prototype and evaluate its performance in a system simulation based on a hardware description language (HDL). For the computation of a simple SANN for image segmentation, the NeuroPipe-Chip operating at 100 MHz shows an improvement of more than two orders of magnitude compared to an Alpha 500 MHz workstation and approaches real-time requirements for the computation of SANNs in the order of 10/sup 6/ neurons. Hence, such an accelerator would allow for applications of complex SANNs to solve real-world tasks like real-time image processing. The NeuroPipe-Chip has been fabricated in an Alcatel 0.35-/spl mu/m digital CMOS technology.
['Tim Schoenauer', 'Sahin Atasoy', 'Nasser Mehrtash', 'Heinrich Klar']
NeuroPipe-Chip: A digital neuro-processor for spiking neural networks