DIGITAL NEURAL NETWORKS FOR HIGH-SPEED DIVISIONS AND ROOT EXTRACTIONS

Abstract

The instruction set of digital integer-valued VLSIs implementing neural networks is typically restricted to weighted sums, threshold activation functions and weight assignments. However, neural networks also need divisions (for data normalizations) and root extractions (for distance or vector norm computations). This paper presents neural networks which perform the latter operations by using only the above basic instructions. These networks thus provide "macros" to the overall network which calls them. This yields a homogeneous environment. Moreover, a "virtual base" is introduced to exploit the available parallelism. It is freely chosen so as to achieve the desired trade-off between the speed and complexity of the proposed networks (with a minimum of only two neurons). This high-speed capability also makes these structures attractive independently from neural applications.