Multiplierless neural networks for application to digital video

Advances in artificial neural networks have led to several applications such as in computer-vision, image restoration, speech recognition, and pattern classification. However, for widespread practical use these and many other potential applications require large, high-speed networks implemented in efficient custom hardware. This paper investigates an important issue in the implementation of such networks, namely the avoidance of multipliers. Secondly, it evaluates their application in digital video, specifically in high-performance motion prediction and frame reconstruction. The authors discuss the multilayer perceptron and Hopfield neural networks in particular.