Artificial neural networks as building blocks of mixed signal FPGA

Author

Manjunath, R. ; Gurumurthy, K.S.

Author_Institution

Dept. EC & CSE, UVCE, Bangalore, India

fYear

2003

fDate

15-17 Dec. 2003

Firstpage

375

Lastpage

378

Abstract

Ever since the deployment of FPAAs, efforts are on the way to minimize the silicon area to realize an arbitrary system. A relatively new concept which has been tested and tried in this direction is the use of Artificial neural networks (ANNs) as Configurable Analog Blocks (CABs). Conventional ANNs however suffer with lengthy training period. In this paper ANNs with differential feedback technique are explored. It has been found out that they perform better than the conventional ANNs.

Keywords

feedback; field programmable analogue arrays; field programmable gate arrays; learning (artificial intelligence); mixed analogue-digital integrated circuits; neural nets; ANN; CAB; FPAA; Si; arbitrary system; artificial neural networks; configurable analog blocks; differential feedback technique; field programmable analogue arrays; field programmable gate arrays; mixed signal FPGA; Artificial neural networks; Circuits; Equations; Field programmable analog arrays; Field programmable gate arrays; Neural network hardware; Neurofeedback; Neurons; Silicon; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Field-Programmable Technology (FPT), 2003. Proceedings. 2003 IEEE International Conference on

Print_ISBN

0-7803-8320-6

Type

conf

DOI

10.1109/FPT.2003.1275780

Filename

1275780