Abstract
Incremental path-based timing analysis (PBA) is a pivotal step in the timing optimization flow. A core building block analyzes the timing path-by-path subject to a critical amount of incremental changes on the design. However, this process in nature demands an extremely high computational complexity and has been a major bottleneck in accelerating timing closure. Therefore, we introduce in this paper a fast and scalable algorithm of incremental PBA with MapReduce – a recently popular programming paradigm in big-data era. Inspired by the spirit of MapReduce, we formulate our problem into tasks that are associated with keys and values and perform massively-parallel map and reduce operations on a distributed system. Experimental results demonstrated that our approach can not only easily analyze huge deisgns in a few minutes, but also quickly revalidate the timing after the incremental changes. Our results are beneficial for speeding up the lengthy design cycle of timing closure.
Original language | English |
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Title of host publication | 2015 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP) |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 9781467381895 |
DOIs | |
Publication status | Published - Jun 2015 |
Event | 2015 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP) - San Francisco, United States Duration: 6 Jun 2015 → 6 Jun 2015 https://ieeexplore.ieee.org/xpl/conhome/7160953/proceeding (Link to conference proceedings) |
Publication series
Name | Proceedings of ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP) |
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Conference
Conference | 2015 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP) |
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Country/Territory | United States |
City | San Francisco |
Period | 6/06/15 → 6/06/15 |
Internet address |
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