July
1988
Neural network simulation at Warp speed: How we got 17 million connections per second
Authors:
Abstract:
A fast back-propagation algorithm for a linear array of processors is described. Results of an implementation of this algorithm on Warp, a ten-processor, programmable systolic array computer, are reviewed and compared with back-propagation implementations on other machines. The current Warp simulator is about eight times faster at simulating the NETtalk text-to-speech network than the fastest back-propagation simulator previously reported in the literature. This fast simulator on Warp is being used routinely in a road-recognition experiment for robot navigation. Results indicate that linear systolic array machines can be efficient neural network simulators. Planned extensions and improvements to the current algorithm are discussed.
@conference{Pomerleau-1988-15414,
author = {Dean Pomerleau And G. L. Gusciora And David S. Touretzky And H. T. Kung},
title = {Neural network simulation at Warp speed: How we got 17 million connections per second},
booktitle = {Proceedings of IEEE International Joint Conference on Neural Networks},
year = {1988},
month = {July},
volume = {2},
pages = {143 - 150},
}
author = {Dean Pomerleau And G. L. Gusciora And David S. Touretzky And H. T. Kung},
title = {Neural network simulation at Warp speed: How we got 17 million connections per second},
booktitle = {Proceedings of IEEE International Joint Conference on Neural Networks},
year = {1988},
month = {July},
volume = {2},
pages = {143 - 150},
}