Defect-Aware High-Level Synthesis Targeted at Reconfigurable Nanofabrics

Entering the nanometer era, a major challenge to current design methodologies and tools is how to effectively address the high defect densities projected for nanoelectronic technologies. To this end, a reconfiguration-based defect-avoidance methodology for defect-prone nanofabrics was proposed. 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, a synthesis framework aimed at implementing this new design paradigm is proposed. A key novelty of the approach with respect to traditional high-level synthesis (HLS) is that, rather than carefully optimizing a single ("deterministic") solution, the 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 average performance of the family of synthesized solutions. Experimental results generated for a set of representative benchmark kernels, assuming different defect regimes and target yields, empirically show that the proposed algorithms can effectively explore the complex probabilistic design space associated with this new class of HLS problems