A robust algorithm for approximate Compatible Observability Don't Care (CODC) computation
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Compatible Observability Don't Cares (CODCs) are a powerful means to express the flexibility present at a node in a multi-level logic network. Despite their elegance, the applicability of CODCs has been hampered by their computational complexity. The CODC computation for a network involves several image computations, which require the construction of global BDDs of the circuit nodes. The size of BDDs of circuit nodes is unpredictable, and as a result, the CODC computation is not robust. In practice, CODCs cannot be computed for large circuits due to this limitation. In this paper, we present an algorithm to compute approximate CODCs (ACODCs). This algorithm allows us to compute compatible don't cares for significantly larger designs. Our ACODC algorithm is scalable in the sense that the user may trade off time and memory against the accuracy of the ACODCs computed. The ACODC is computed by considering a subnetwork rooted at the node of interest, up to a certain topological depth, and performing its don't care computation. We prove that the ACODC is an approximation of its CODC. We have proved the soundness of the approach, and performed extensive experiments to explore the trade-off between memory utilization, speed and accuracy. We show that even for small topological depths, the ACODC computation gives very good results. Our experiments demonstrate that our algorithm can compute ACODCs for circuits whose CODC computation has not been demonstrated to date. Also, for a set of benchmark circuits whose CODC computation yields an average 28% reduction in literals after optimization, our ACODC computation yields an average 22% literal reduction. Our algorithm has runtimes which are about 25 and memory utilization which is 33 better that of the CODC computation of SIS.
