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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Defect-Tolerant CMOL Cell Assignment via Satisfiability
William N. N. Hung, Changjian Gao, Xiaoyu Song, Dan Hammerstrom · 2007-05-30 · via cs.DS updates on arXiv.org

We present a CAD framework for CMOL, a hybrid CMOS/ molecular circuit architecture. Our framework first transforms any logically synthesized circuit based on AND/OR/NOT gates to a NOR gate circuit, and then maps the NOR gates to CMOL. We encode the CMOL cell assignment problem as boolean conditions. The boolean constraint is satisfiable if and only if there is a way to map all the NOR gates to the CMOL cells. We further investigate various types of static defects for the CMOL architecture, and propose a reconfiguration technique that can deal with these defects through our CAD framework. This is the first automated framework for CMOL cell assignment, and the first to model several different CMOL static defects. Empirical results show that our approach is efficient and scalable.