FPGA implementation of biologically-inspired auto-associative memory

Chong H. Ang; Alistair McEwan; André van Schaik; Craig Jin; Philip H. W. Leong

doi:10.1049/el.2011.3651

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FPGA implementation of biologically-inspired auto-associative memory

Chong H. Ang Alistair McEwan André van Schaik Craig Jin Philip H. W. Leong

Year: 2012 Journal: Electronics Letters Vol: 48 (3)Pages: 148-149 Publisher: Institution of Engineering and Technology

DOI: 10.1049/el.2011.3651

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Abstract

The design of an auto-associative memory based on a spiking neural network is described. Delays rather than binary values are used to represent signals and coincidence is used to perform pattern matching. A complete implementation of the memory on a single FPGA is presented.

Keywords:

Field-programmable gate array Content-addressable memory Computer science Bidirectional associative memory Associative property Content-addressable storage Artificial neural network Binary number Memory map Matching (statistics) Computer hardware Embedded system Artificial intelligence Arithmetic Semiconductor memory Mathematics

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Neural Networks and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Neural dynamics and brain function

Life Sciences → Neuroscience → Cognitive Neuroscience

Advanced Memory and Neural Computing

Physical Sciences → Engineering → Electrical and Electronic Engineering

FPGA implementation of biologically-inspired auto-associative memory

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