Evolutionary morphogenesis for multi-cellular systems
Genetic Programming and Evolvable Machines
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Spiking neurons computing platform
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
| Hi-index | 0.00 |
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 106 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-μm digital CMOS technology