Dataflow Architectures: Flexible Platforms for Neural Network Simulation
Abstract
Dataflow architectures are general computation engines optimized for the execution of fme-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.
Cite
Text
Smotroff. "Dataflow Architectures: Flexible Platforms for Neural Network Simulation." Neural Information Processing Systems, 1989.Markdown
[Smotroff. "Dataflow Architectures: Flexible Platforms for Neural Network Simulation." Neural Information Processing Systems, 1989.](https://mlanthology.org/neurips/1989/smotroff1989neurips-dataflow/)BibTeX
@inproceedings{smotroff1989neurips-dataflow,
title = {{Dataflow Architectures: Flexible Platforms for Neural Network Simulation}},
author = {Smotroff, Ira},
booktitle = {Neural Information Processing Systems},
year = {1989},
pages = {818-825},
url = {https://mlanthology.org/neurips/1989/smotroff1989neurips-dataflow/}
}