Dataflow Architectures: Flexible Platforms for Neural Network Simulation
Ira G. Smotroff · Neural Information Processing Systems · 1989
Dataflow architectures are general computation engines optimized for the execution of fine-grain parallel algorithms. Neural networks can be simulated on these systems with certain advantages. In this paper, we review dataflow architectures, examine neural network simulation performance on a new generation dataflow machine, compare that performance to other simulation alternatives, and discuss the benefits and drawbacks of the dataflow approach.