Affiliation:
1. Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, Práter u. 50/A, 1083 Budapest, Hungary
Abstract
In this paper, we demonstrate that physical waves can be used to perform convolutions as part of a state-of-the-art neural network architecture. In particular, we show that the damping of waves, which is unavoidable in a physical implementation, does not diminish their usefulness in performing the convolution operations required in a convolutional neural network (CNN), and the damping only slightly decreases the classification accuracy of the network. These results open the door for wave-based hardware accelerators for CNNs.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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