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

fYear

1993

Firstpage

560

Lastpage

569

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;

fLanguage

English

Publisher

ieee

Conference_Titel

ASIC Conference and Exhibit, 1993. Proceedings., Sixth Annual IEEE International

Conference_Location

Rochester, NY

Print_ISBN

0-7803-1375-5

Type

conf

DOI

10.1109/ASIC.1993.410845

Filename

410845