Title :
VLSI neural network architectures
Author :
Sridhar, Ramalingam ; Shin, Yong-Chul
Author_Institution :
Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA
Abstract :
VLSI architectures for neural networks are presented. Neural networks have wide-ranging applications in classification, control, and optimization. With the need for real-time performance, VLSI neural networks have gained significant attention. Digital, analog, and mixed-mode designs are used for this application. Modular and reconfigurable designs are necessary so that various neural network models can be easily configured
Keywords :
VLSI; analogue processing circuits; application specific integrated circuits; content-addressable storage; mixed analogue-digital integrated circuits; neural chips; neural net architecture; reconfigurable architectures; VLSI architectures; analog designs; associative memory; chip implementations; digital designs; mixed-mode designs; neural networks; on-chip learning; reconfigurable ASIC; reconfigurable designs; tutorial; Application software; Artificial neural networks; Character recognition; Computer architecture; Computer networks; Fault detection; Image classification; Neural networks; Neurons; Very large scale integration;
Conference_Titel :
ASIC Conference and Exhibit, 1993. Proceedings., Sixth Annual IEEE International
Conference_Location :
Rochester, NY
Print_ISBN :
0-7803-1375-5
DOI :
10.1109/ASIC.1993.410845
