RAS-NANO: A Reliability-Aware Synthesis Framework for Reconfigurable Nanofabrics

Entering the nanometer era, a major challenge to current design methodologies and tools is to effectively address the high defect densities projected for nanotechnologies. To this end, we proposed a reconfiguration-based defect-avoidance methodology for defect-prone nanofabrics. It judiciously architects the nanofabric, using probabilistic considerations, such that a very large number of alternative implementations can be mapped into it, enabling defects to be circumvented at configuration time in a scalable way. Building on this foundation, in this paper we propose a synthesis framework aimed at implementing this new design paradigm. A key novelty of our approach with respect to traditional high level synthesis is that, rather than carefully optimizing a single (`deterministic´) solution, our goal is to simultaneously synthesize a large family of alternative solutions, so as to meet the required probability of successful configuration, or yield, while maximizing the family´s average performance. Experimental results generated for a set of representative benchmark kernels, assuming different defect regimes and target yields, empirically show that our proposed algorithms can effectively explore the complex probabilistic design space associated with this new class of high level synthesis problems