DocumentCode :
3273700
Title :
An artificial neural network accelerator for pulse coded model-neurons
Author :
Frank, G. ; Hartmann, G.
Author_Institution :
Dept. of Electr. & Electron. Eng., Paderborn Univ., Germany
Volume :
4
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
2014
Abstract :
The following report introduces the hardware design of a neuro-computer aligned to simulate large neural nets consisting of pulse-coded model neurons. The simulation process is carried out in real-time, referring to image processing. A single accelerator provides 32k neurons with 128 synapses each. There is no need to assign a fixed number of synaptic weights to each neuron rather then distributing the total amount of four million synapses arbitrary to any neuron. The simulation of larger nets is possible by connecting the accelerators in a hexagonal structure, where the simulation time will only increase if the overall activity of the net mainly effects one specific accelerator board
Keywords :
computer vision; image processing; neural nets; object recognition; real-time systems; computer vision; image processing; neural network accelerator; neuro-computer; object recognition; pulse coded model-neurons; real-time system; simulation process; synaptic weights; Artificial neural networks; Biological system modeling; Computational modeling; Electron accelerators; Hardware; Humans; Joining processes; Neurons; Object recognition; Visual system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.488982
Filename :
488982
Link To Document :
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