Efficient implementation of neural networks on the DREAM machine

Abstract

High-throughput implementations of neural networks are needed in order to expand the use of this technology from small research problems into practical 'real-world' applications. Due to the wide range of possible neural network paradigms and the rapid evolution of these models, high degree of implementation flexibility if essential. The Dynamically Reconfigurable Extended Array Multiprocessor (DREAM) Machine has been specifically designed for implementation of neural networks. The architecture offers sufficient flexibility for use on a wide range of neural network applications. The authors describe the basic computational and communicational requirements of neural network models. A mapping method is proposed that can take advantage of the DREAM Machine's reconfigurable interconnection network to achieve efficient implementations for a diverse range of neural network structures. The effectiveness of the architecture and the mapping method is demonstrated through the use of several examples and shown to be superior to previous systolic implementation methods. >