Logic synthesis and testing techniques for switching nano-crossbar arrays

Beyond CMOS, new technologies are emerging to extend electronic systems with features unavailable to silicon-based devices. Emerging technologies provide new logic and interconnection structures for computation, storage and communication that may require new design paradigms, and therefore trigger the development of a new generation of design automation tools. In the last decade, several emerging technologies have been proposed and the time has come for studying new ad-hoc techniques and tools for logic synthesis, physical design and testing. The main goal of this project is developing a complete synthesis and optimization methodology for switching nano-crossbar arrays that leads to the design and construction of an emerging nanocomputer. New models for diode, FET, and four-terminal switch based nanoarrays are developed. The proposed methodology implements logic, arithmetic, and memory elements by considering performance parameters such as area, delay, power dissipation, and reliability. With combination of logic, arithmetic, and memory elements a synchronous state machine (SSM), representation of a computer, is realized. The proposed methodology targets variety of emerging technologies including nanowire/nanotube crossbar arrays, magnetic switch-based structures, and crossbar memories. The results of this project will be a foundation of nano-crossbar based circuit design techniques and greatly contribute to the construction of emerging computers beyond CMOS. The topic of this project can be considered under the research area of â\u80\u9cEmerging Computing Modelsâ\u80\u9d or â\u80\u9cComputational Nanoelectronicsâ\u80\u9d, more specifically the design, modeling, and simulation of new nanoscale switches beyond CMOS

A survey of fault-tolerance algorithms for reconfigurable nano-crossbar arrays

ACM Comput. Surv. Volume 50, issue 6 (November 2017)Nano-crossbar arrays have emerged as a promising and viable technology to improve computing performance of electronic circuits beyond the limits of current CMOS. Arrays offer both structural efficiency with reconfiguration and prospective capability of integration with different technologies. However, certain problems need to be addressed, and the most important one is the prevailing occurrence of faults. Considering fault rate projections as high as 20% that is much higher than those of CMOS, it is fair to expect sophisticated fault-tolerance methods. The focus of this survey article is the assessment and evaluation of these methods and related algorithms applied in logic mapping and configuration processes. As a start, we concisely explain reconfigurable nano-crossbar arrays with their fault characteristics and models. Following that, we demonstrate configuration techniques of the arrays in the presence of permanent faults and elaborate on two main fault-tolerance methodologies, namely defect-unaware and defect-aware approaches, with a short review on advantages and disadvantages. For both methodologies, we present detailed experimental results of related algorithms regarding their strengths and weaknesses with a comprehensive yield, success rate and runtime analysis. Next, we overview fault-tolerance approaches for transient faults. As a conclusion, we overview the proposed algorithms with future directions and upcoming challenges.This work is supported by the EU-H2020-RISE project NANOxCOMP no 691178 and the TUBITAK-Career project no 113E760

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