A board system for high-speed image analysis and neural networks

Author

Säckinger, Eduard ; Graf, Hans-Peter

Author_Institution

AT&T Bell Labs., Holmdel, NJ, USA

Volume

7

Issue

1

fYear

1996

fDate

1/1/1996 12:00:00 AM

Firstpage

214

Lastpage

221

Abstract

Two ANNA neural-network chips are integrated on a 6U VME board, to serve as a high-speed platform for a wide variety of algorithms used in neural-network applications as well as in image analysis. The system can implement neural networks of variable sizes and architectures, but can also be used for filtering and feature extraction tasks that are based on convolutions. The board contains a controller implemented with field programmable gate arrays (FPGA´s), memory, and bus interfaces, all designed to support the high compute power of the ANNA chips. This new system is designed for maximum speed and is roughly 10 times faster than a previous board. The system has been tested for such tasks as text location, character recognition, and noise removal as well as for emulating cellular neural networks (CNN´s). A sustained speed of up to two billion connections per second (GC/s) and a recognition speed of 1000 characters per second has been measured

Keywords

cellular neural nets; convolution; feature extraction; field programmable gate arrays; image processing equipment; mixed analogue-digital integrated circuits; neural chips; optical character recognition; printed circuit design; 6U VME board; ANNA neural-network chips; FPGA; board system; bus interfaces; cellular neural network emulation; character recognition; convolutions; feature extraction tasks; field programmable gate arrays; filtering; high-speed image analysis; high-speed platform; memory; noise removal; text location; Cellular neural networks; Character recognition; Computer architecture; Computer interfaces; Feature extraction; Field programmable gate arrays; Filtering; Image analysis; Neural networks; System testing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.478407

Filename

478407