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Today's automotive industry is making a bold move to equip vehicles with intelligent driver assistance features. A modern automobile is now equipped with a powerful computing platform to run multiple machine learning algorithms for environment perception (e.g., pedestrian detection) and motion control (e.g., vehicle stabilization). These machine learning systems must be highly robust with extremely...
Ambient occlusion is an illumination simulation approach used in ray tracing for decades. However, huge consume of multiple sample times, for a high re-solution, in ambient occlusion restricts its development. We propose an algorithm decreasing the sample times with adjacent pixels shared information in sampling process. By introducing this nova approach, we get a big improvement when comparing to...
In this paper, we propose a novel subset simulation (SUS) technique to efficiently estimate the rare failure rate for nanoscale circuit blocks (e.g., SRAM, DFF, etc.) in high-dimensional variation space. The key idea of SUS is to express the rare failure probability of a given circuit as the product of several large conditional probabilities by introducing a number of intermediate failure events....
Statistical analysis of SRAM has emerged as a challenging issue because the failure rate of SRAM cells is extremely small. In this paper, we develop an efficient importance sampling algorithm to capture the rare failure event of SRAM cells. In particular, we adapt the Gibbs sampling technique from the statistics community to find the optimal probability distribution for importance sampling with a...
Statistical analysis of SRAM has emerged as a challenging issue because the failure rate of SRAM cells is extremely small. In this paper, we develop an efficient importance sampling algorithm to capture the rare failure event of SRAM cells. In particular, we adapt the Gibbs sampling technique from the statistics community to find the optimal probability distribution for importance sampling with minimum...
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