A parallel processing VLSI BAM engine

Abstract

In this paper emerging parallel/distributed architectures are explored for the digital VLSI implementation of adaptive bidirectional associative memory (BAM) neural network. A single instruction stream many data stream (SIMD)-based parallel processing architecture, is developed for the adaptive BAM neural network, taking advantage of the inherent parallelism in BAM. This novel neural processor architecture is named the sliding feeder BAM array processor (SLiFBAM). The SLiFBAM processor can be viewed as a two-stroke neural processing engine, It has four operating modes: learn pattern, evaluate pattern, read weight, and write weight. Design of a SLiFBAM VLSI processor chip is also described. By using 2-mum scalable CMOS technology, a SLiFBAM processor chip with 4+4 neurons and eight modules of 256x5 bit local weight-storage SRAM, was integrated on a 6.9x7.4 mm(2) prototype die. The system architecture is highly flexible and modular, enabling the construction of larger BAM networks of up to 252 neurons using multiple SLiFBAM chips.

Global stabilization of a class of fractional-order delayed bidirectional associative memory neural networks

“…In this section, we focus on the global stabilization of the system of Eq. (5). A kind of control scheme is proposed based on feedback control, and a sufficient condition is derived to achieve the global stabilization of the system.…”

Uso de um Ambiente Codesign para a Implementação de Redes Neurais

“…Para a implementação em hardware dedicado pode-se abordar a forma do projeto sob três focos distintos. O primeiro foco utiliza técnicas digitais de implementação, [2], o segundo foco aborda as técnicas analógicas, como pode ser examinado em [3], [4], [5], [6], [7] e [8]; e por fim, o terceiro foco se utiliza das técnicas híbridas, como em [9] e em [10], tendo a implementação composta em parte digital e em parte analógica.…”

Stability Analysis of Fractional‐Order Bidirectional Associative Memory Neural Networks with Mixed Time‐Varying Delays

“…A BAM neural network consists of two layers of associative neurons, and the neurons arranged in one layer are fully interconnected with those in the other layer, but there are no interconnections in the same layer. It has been revealed that BAM neural networks can provide potential applications in pattern recognition, signal processing, and combinatorial optimization [6,7].…”