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| Content Provider | ACM Digital Library |
|---|---|
| Author | Callegari, Nicholas Tetelbaum, Alexander Bastani, Pouria Chakravarty, Sreejit Wang, Li-C. |
| Abstract | This paper presents a novel path selection methodology to select paths for monitoring unexpected systematic timing effects. The methodology consists of three components: path filtering, path encoding, and path clustering. Given a large set of critical paths, in path filtering, the goal is to filter out paths that cannot be functionally sensitized. To explore the space of unexpected timing effects, a set of features are defined to encode paths into path vectors. Each feature is a source of concern that may potentially contribute to the cause of an unexpected timing effect. Finally, a kernel-based clustering algorithm is employed to group similar path vectors into clusters from which the best representative paths are selected for post-silicon monitoring. The effectiveness of our proposed methodology is demonstrated through experiments on an industrial ASIC design. |
| Starting Page | 781 |
| Ending Page | 786 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424427482 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-01-19 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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