Defect-Aware Nanocrossbar Logic Mapping through Matrix Canonization Using Two-Dimensional Radix Sort

Abstract

Nanocrossbars (i.e., nanowire crossbars) offer extreme logic densities but come with very high defect rates; stuck-open/closed, broken nanowires. Achieving reasonable yield and utilization requires logic mapping that is defect-aware even at the crosspoint level. Such logic mapping works with a defect map per each manufactured chip. The problem can be expressed as matching of two bipartite graphs; one for the logic to be implemented and other for the nanocrossbar. This article shows that the problem becomes a Bipartite SubGraph Isomorphism (BSGI) problem within sub-nanocrossbars free of stuck-closed faults. Our heuristic KNS-2DS is an iterative rough canonizer with approximately O(N 2 ) complexity followed by an O(N 3 ) matching algorithm. Canonization brings a partial or full order to graph nodes. It is normally used for solving the regular Graph Isomorphism (GI) problem, while we apply it to BSGI. KNS stands for K-Neighbor Sort and is used for initializing our main contribution 2-Dimensional-Sort (2DS). 2DS operates on the adjacency matrix of a bipartite graph. Radix-2 2DS solves the problem in the absence of stuck-closed faults. With the addition of Radix-3 and our novel Radix-2.5 sort, we solve problems that also have stuck-closed faults. We offer very short runtimes (due to canonization) compared to previous work and have success on all benchmarks. KNS-2DS is also novel from the perspective of BSGI problem as it is based on canonization but not on a search tree with backtracking.