Title :
Mixed analogue-digital artificial-neural-network architecture with on-chip learning
Author :
Schmid, A. ; Leblebici, Y. ; Mlynek, D.
Author_Institution :
Dept. of Electr. Eng., Swiss Fed. Inst. of Technol., Lausanne, Switzerland
fDate :
12/1/1999 12:00:00 AM
Abstract :
The authors present a novel artificial-neural-network architecture with on-chip learning capability. The issue of straightforward design-flow integration of an autonomous unit is addressed with a mixed analogue-digital approach, by implementing a charge-based artificial neural network which interacts with digital control and processing units. The circuit architecture and design-flow approach for the case of a Hamming network performing pixel-pattern recognition are described
Keywords :
hardware description languages; integrated circuit design; learning (artificial intelligence); mixed analogue-digital integrated circuits; neural chips; reconfigurable architectures; Hamming network; autonomous unit; circuit architecture; design-flow integration; digital control; digital processing units; mixed analogue-digital artificial-neural-network architecture; on-chip learning; reconfigurable architecture;
Journal_Title :
Circuits, Devices and Systems, IEE Proceedings -
DOI :
10.1049/ip-cds:19990685