A Fast Hill Climbing Algorithm for Defect and Variation Tolerant Logic Mapping of Nano-Crossbar Arrays

Abstract

Nano-crossbar arrays are area and power efficient structures, generally realized with self-assembly based bottom-up fabrication methods as opposed to relatively costly traditional top-down lithography techniques. This advantage comes with a price: very high process variations. In this work, we focus on the worst-case delay optimization problem in the presence of high process variations. As a variation tolerant logic mapping scheme, a fast hill climbing algorithm is proposed; it offers similar or better delay improvements with much smaller runtimes compared to the methods in the literature. Our algorithm first performs a reducing operation for the crossbar motivated by the fact that the whole crossbar is not necessarily needed for the problem. This significantly decreases the computational load up to 72 percent for benchmark functions. Next, initial column mapping is applied. After the first two steps that can be considered as preparatory, the algorithm proceeds to the last step of hill climbing row search with column reordering where optimization for variation tolerance is performed. As an extension to this work, we directly apply our hill climbing algorithm on defective arrays to perform both defect and variation tolerance. Again, simulation results approve the speed of our algorithm, up to 600 times higher compared to the related algorithms in the literature without sacrificing defect and variation tolerance performance.