@inproceedings{779cf4b5d30746a9bb4f961336cb3736,
title = "VLSI implementation of a neural network classifier based on the saturating linear activation function",
abstract = "This paper presents a digital VLSI implementation of a feedforward neural network classifier based on the saturating linear activation function. The architecture consists of one-hidden layer performing the weighted sum followed by a saturating linear activation function. The hardware implementation of such a network presents a significant advantage in terms of circuit complexity as compared to a network based on a sigmoid activation function, but without compromising the classification performance. Simulation results on two benchmark problems show that feedforward neural networks with the saturating linearity perform as well as networks with the sigmoid activation function. The architecture can also handle variable precision resulting in a higher computational resources at lower precision.",
author = "A. Bermak and A. Bouzerdoum",
note = "Publisher Copyright: {\textcopyright} 2002 Nanyang Technological University.; 9th International Conference on Neural Information Processing, ICONIP 2002 ; Conference date: 18-11-2002 Through 22-11-2002",
year = "2002",
doi = "10.1109/ICONIP.2002.1198207",
language = "English",
series = "ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "981--985",
editor = "Lipo Wang and Kunihiko Fukushima and Rajapakse, {Jagath C.} and Soo-Young Lee and Xin Yao",
booktitle = "ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing",
address = "United States",
}