VLSI implementation of a neural network classifier based on the saturating linear activation function
Document Type
Conference Proceeding
Publisher
IEEE
Faculty
Faculty of Computing, Health and Science
School
School of Engineering and Mathematics
RAS ID
19
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.
DOI
10.1109/ICONIP.2002.1198207
Comments
Bermak, A., & Bouzerdoum, A. (2002, November). VLSI implementation of a neural network classifier based on the saturating linear activation function. In Neural Information Processing, 2002. ICONIP'02. Proceedings of the 9th International Conference on (Vol. 2, pp. 981-985). IEEE. Available here