Name
Abstract | ||
|---|---|---|
Some of the fastest thermal estimation techniques at the architectural level are based on Green’s functions (impulse response of a unit power source). The resultant temperature profile can be easily obtained by computing a convolution of the Green’s function and the power profile. Sadly, existing approaches do not take process and temperature variation into account, which are integral aspects of today’s technologies. This problem is still open. In this paper, we provide a closed-form solution for the Green’s function after taking process, temperature, and thermal conductivity variation into account. Moreover, during the process of computing the thermal map, we reduce the amount of redundant work by identifying similar regions in the chip using an unsupervised learning-based approach. We were able to obtain a 700,000X speedup over state-of-the-art proposals with a mean absolute error limited to 0.7
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C (1.5%). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DAC18072.2020.9218754 | 2020 57th ACM/IEEE Design Automation Conference (DAC) |
DocType | ISSN | ISBN |
Conference | 0738-100X | 978-1-7281-1085-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hameedah Sultan | 1 | 6 | 3.12 |
| Smruti R. Sarangi | 2 | 447 | 41.94 |