IPSA: Integer Programming via Sparse Approximation for Efficient TestChip Design Logic test chips are a key component of the yield learning process, which aim to investigate the yield characteristics of actual products that will be fabricated at high volume. Mathematically, the design of a logic test chip with such an objective may involve solving a constrained underdetermined equation for an integer vector solution, which is unfortunately, NPhard. Existing solving methods are not applicable due to lack of accuracy or high computational complexity. We propose a method called IPSA (Integer Programming via Sparse Approximation) to solve this integer programming (IP) problem in an effective and efficient manner. By solving a transformed sparseregression problem and a subsequent rounding process, a solution can be achieved with comparable error to the optimal solution of the original IP problem but using far less time and memory. Experiments with seven industrial examples demonstrate that with more than 100xc3x97 speedup, IPSA achieves a similar or even better solution compared to directly solving the original problem with a commercial IP solver.

Area Optimization of MPRM Circuits Using Approximate Computing Approximate computing is a novel paradigm design of Integrated Circuits (ICs). By introducing an acceptable amount of inaccuracy, the area, power, and delay of a circuit can be significantly reduced. In this paper, an approximate logic synthesis (ALS) method target for circuit area optimization is proposed for mixedpolarity ReedMuller (MPRM) circuits. The proposed algorithm mainly consists of the method of the error rate(ER) computing of ReedMuller (RM) functions using disjointed products and the approach of the approximate MPRM functions searching for more compacted forms under the given error rate constraint. The experiments on benchmarks showed that our method is better than those which only use bitwise operation without introducing any errors to the functions. And by using the proposed algorithm, the average circuitsu0027 area can be reduced by 49.71% with the average error rate of 1.73%. Furthermore, it can work fast and it is more efficient for large functions.