An architecture, systems and methods for a scalable artificial neural network, wherein the
architecture includes: an input layer; at least one hidden layer; an output layer; and a …

Abstract Systems and methods for a scalable artificial neural network, wherein the
architecture includes: an input layer; at least one hidden layer; an output layer; and a …

The artificial neural networks (ANNs) have been used successfully in applications such as
pattern recognition, image processing, automation and control. Majority of today's …

The associative Hopfield memory is a form of recurrent Artificial Neural Network (ANN) that
can be used in applications such as pattern recognition, noise removal, information retrieval …

This paper presents a new efficient parallel implementation of neural networks on mesh-
connected SIMD machines. A new algorithm to implement the recall and training phases of …

This paper addresses the problem of mapping a feedforward ANN onto a multiple bus
system, MBS, with p processors and b buses so as to minimize the total execution time. We …

This study analyses how we can optimally implement the backpropagation training algorithm
on a parallel architectures with heterogeneous processors. We use pattern-partitioning …

In recent years, more and more researchers have been aware of the effectiveness of using
the extended Kalman filter (EKF) in neural network learning since some information such as …

The paper describes an efficient and fast systolic architecture for the implementation of
artificial neural networks. The proposed architecture, TORAN (two-in-one ring array …

In this paper, an effective memory-processor integrated architecture, called memory-based
processor array for artificial neural networks (MPAA), is proposed. The MPAA can be easily …