Artificial neural networks (ANNs) have many characteristics that are suitable for massively parallel computation: simple processing units (neurons), small local memory requirement for each neuron, highly parallel operations. Naturally, neural network implementation should be a target for massively parallel computing. This paper presents and discusses techniques to map a neural network algorithm onto a massively parallel computer system. The goal is to maximize parallelism by breaking the ANN computation into basic units and processing these units in parallel. The following strategies are discussed: (1) design special highly parallel computers for artificial neural networks; (2) map the neural network algorithms directly onto the existing general-purpose parallel computers; (3) map the neural network algorithms onto the optical bus based systems; (4) design new structured neural networks that are similar to the topologies of the existing parallel systems; (5) use the divide-and-conquer technique to break a large neural network into many small ones, each will be processed by a PC or workstation.
On embeddings of neural networks into massively parallel computer systems
01.01.1997
680272 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
On Embedding of Neural Networks into Massively Parallel Computer Systems
British Library Conference Proceedings | 1997
|Scalable Massively Parallel Artificial Neural Networks
AIAA | 2008
|Scalable Massively Parallel Artificial Neural Networks
AIAA | 2005
|Optical Interconnections for the Massively Parallel Computer
British Library Conference Proceedings | 1996
|