Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. By performing beam search in a tree-structured space, AutoCross enables efficient generation of high-order cross features, which is not yet visited by existing works. Additionally, we propose successive mini-batch gradient descent and multi-granularity discretization to further improve efficiency and effectiveness, while ensuring simplicity so that no machine learning expertise or tedious hyper-parameter tuning is required. Furthermore, the algorithms are designed to reduce the computational, transmitting, and storage costs involved in distributed computing. Experimental results on both benchmark and real-world business datasets demonstrate the effectiveness and efficiency of AutoCross. It is shown that AutoCross can significantly enhance the performance of both linear and deep models.

In this paper, a Bayesian optimization approach is proposed for yield optimization of analog and SRAM circuits. Gaussian process (GP) regression is employed to predict the yield over the design space with uncertainty information. An expected improvement acquisition function is constructed over the model and guides the optimization with a utility-based strategy. These techniques, as a whole, can significantly reduce the number of expensive yield estimations during the optimization procedure. Furthermore, the GP model encodes the observation uncertainties of noise-corrupted objectives, which enables an adaptive control over yield estimations. By ensuring high estimation accuracies for promising designs while tolerating higher variabilities for low-yield ones, the proposed method can significantly cut down the average computational cost of yield estimations without surrendering the accuracy of the final result. Experimental results show that, compared with the state-of-the-art yield optimization approaches, the proposed method can significantly reduce the number of circuit simulations without compromising optimization efficacy.

Conventional yield optimization approaches rely on accurate yield estimation for given design parameters, which would be computational intensive. In this paper, a novel Bayesian yield optimization approach is proposed for analog and SRAM circuits. An equivalent a problem is formulated via applying Bayes' theorem on the augmented yield problem. The yield optimization problem is converted to identifying the design parameters with maximal probability density conditioning on the event that the corresponding circuit is "pass". Gaussian kernel density estimation is employed to approximate the conditional probability, and a multi-start-point based EM-like algorithm is proposed to solve the equivalent problem efficiently Compared with the state-of-the-art yield optimization approaches, the proposed method can significantly reduce the number of circuit simulations with comparable optimization accuracy by avoiding repetitive yield estimations.

The failure rate of static RAM (SRAM) cells is restricted to be extremely low to ensure sufficient high yield for the entire chip. In addition, multiple performances of interest and influences from peripherals make SRAM failure rate estimation a high-dimensional multiple-failure-region problem. This paper proposes a new method featuring a multistart-point sequential quadratic programming (SQP) framework to extend minimized norm importance sampling (IS) to address this problem. Failure regions in the variation space are first found by the low-discrepancy sampling sequence. Afterward, start points are generated in all identified failure regions and local optimizations based on SQP are invoked from these start points searching for the optimal shift vectors (OSVs). Based on the OSVs, a Gaussian mixture distorted distribution is constructed for IS. To further reduce the computational cost of IS while fully considering the influence of increasing dimensionality, an adaptive model training framework is proposed to keep high efficiency for both low- and high-dimensional problems. The experimental results show that the proposed method can not only approximate failure rate with high accuracy and efficiency in low-dimensional cases but also keep these features in high-dimensional ones.