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| Content Provider | ACM Digital Library |
|---|---|
| Author | Xiong, Jinjun Zolotov, Vladimir Visweswariah, Chandu Shi, Yiyu |
| Abstract | Increasingly large process variations make selection of a set of critical paths for at-speed testing essential yet challenging. This paper proposes a novel multilayer process space coverage metric to quantitatively gauge the quality of path selection. To overcome the exponential complexity in computing such a metric, this paper reveals its relationship to a concept called order statistics for a set of correlated random variables, efficient computation of which is a hitherto open problem in the literature. This paper then develops an elegant recursive algorithm to compute the order statistics (or the metric) in provable linear time and space. With a novel data structure, the order statistics can also be incrementally updated. By employing a branch-and-bound path selection algorithm with above techniques, this paper shows that selecting an optimal set of paths for a multi-million-gate design can be performed efficiently. Compared to the state-of-the-art, experimental results show both the efficiency of our algorithms and better quality of our path selection. |
| Starting Page | 340 |
| Ending Page | 345 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781605584973 |
| DOI | 10.1145/1629911.1630004 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-07-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Path selection Order statistics Process space coverage |
| Content Type | Text |
| Resource Type | Article |
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