This paper investigates large scale learning algorithms and their implementation on Massively Parallel machines. The system prototype described in this ...

This paper investigates large scale learning algorithms and their implementation on Massively Parallel machines.

This paper describes a framework for implementing neural networks on massively parallel machines. The framework is generic and applies to a range of neural.

Implementation of these algorithms on highly parallel computer with 1024 processors allows high speed character recognition to be achieved at a speed of. 2.3ms/ ...

This paper describes a framework for implementing neural networks on massively parallel machines. The framework is generic and applies to a range of neural ...

Bibliographic details on Neural network implementations and speed-up on Massively Parallel machines.

As a first step toward faster simulation, we focus on the time required to create instances of neuronal network models in the main memory of modern computing ...

The previous section demonstrated main properties of neural network implementations by means ... per, show that these clusters provide only limited speed-up, it ...

in order to speed up the computations. The regular architecture of neural networks suggests that simulations can be implemented on parallel. multiprocessor ...

ABSTRACT. Hopfield neural networks are often used to solve difficult combinatorial optimization problems. Multiple restarts versions find better solutions.