Matrix Nanodevice-Based Logic Architectures and Associated Functional Mapping Method

Abstract

This article describes a novel computing architecture organization based on nanoscale logic cells. We propose the use of a cluster of matrix arrangements of cells. In order to interconnect such fine-grained logic cells within a matrix, conventional techniques are not suitable due to a large interconnect overhead. Therefore, we propose the use of static and incomplete interconnect topologies to create matrices of cells. We also propose a method to map functions onto such architectures. We then explore the main parameters of the structure (size of matrices and interconnect topologies) and their impact on the main performance metrics (packing efficiency, speed, and fault tolerance). A cluster packing method also allows the evaluation of the number of matrices used by complex functions and the fill factor for various matrix sizes. The analyses show that this approach is particularly suited for matrices of 16 cells interconnected by modified omega networks. We can conclude that this architecture could improve the scalability of traditional FPGAs by a factor of 8.5.