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

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.This work is part of a project that has received funding from the European Union’s H2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 691178. This work is supported by the TUBITAK-Career project #113E76

Integrated Synthesis Methodology for Crossbar Arrays

Nano-crossbar arrays have emerged as area and power efficient structures with an aim of achieving high performance computing beyond the limits of current CMOS. Due to the stochastic nature of nano-fabrication, nano arrays show different properties both in structural and physical device levels compared to conventional technologies. Mentioned factors introduce random characteristics that need to be carefully considered by synthesis process. For instance, a competent synthesis methodology must consider basic technology preference for switching elements, defect or fault rates of the given nano switching array and the variation values as well as their effects on performance metrics including power, delay, and area. Presented synthesis methodology in this study comprehensively covers the all specified factors and provides optimization algorithms for each step of the process.This work is part of a project that has received funding from the European Union’s H2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 691178, and supported by the TUBITAK-Career project #113E76

Testability of Switching Lattices in the Cellular Fault Model

A switching lattice is a two-dimensional array of four-terminal switches implemented in its cells. Each switch is linked to the four neighbors and is connected with them when the switch is ON, or is disconnected when the switch is OFF. Recently, with the advent of a variety of emerging nanoscale technologies based on regular arrays of switches, lattices of multi-terminal switches, originally introduced by Akers in 1972, have found a renewed interest. In this paper, the testability under the Cellular Fault Model (CFM) of switching lattices is defined and analyzed. Moreover, some techniques for improving the testability of lattices are discussed and experimentally evaluated