Name
Abstract | ||
|---|---|---|
With the rising challenges in heat removal in integrated circuits (ICs), the development of thermal-aware computing architectures and run-time management systems has become indispensable to the continuation of IC design scaling. These thermal-aware design technologies of the future strongly depend on the availability of efficient and accurate means for thermal modeling and analysis. These thermal models must have not only the sufficient accuracy to capture the complex mechanisms that regulate thermal diffusion in ICs, but also a level of abstraction that allows for their fast execution for design space exploration. In this paper, we propose an innovative thermal modeling approach for full-chips that can handle the scalability problem of transient heat flow simulation in large 2-D/3-D multiprocessor ICs. This is achieved by parallelizing the computation-intensive task of transient temperature tracking using neural networks and exploiting the computational power of massively parallel graphics processing units. Our results show up to $35\times$ run-time speedup compared to state-of-the-art IC thermal simulation tools while keeping the error lower than 1$^{\circ}{\rm C}$. Speedups scale with the size of the 3-D multiprocessor ICs and our proposed method serves as a valuable design space exploration tool. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TCAD.2011.2174236 | IEEE Trans. on CAD of Integrated Circuits and Systems |
Keywords | DocType | Volume |
thermal-aware design technology,thermal diffusion,innovative thermal modeling approach,IC design scaling,thermal modeling,thermal model,design space exploration,3-D multiprocessor ICs,Integrated Circuits,state-of-the-art IC thermal simulation,valuable design space exploration,Thermal Simulation | Journal | 31 |
Issue | ISSN | Citations |
1 | 0278-0070 | 16 |
PageRank | References | Authors |
0.76 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arvind Sridhar | 1 | 101 | 9.04 |
| Alessandro Vincenzi | 2 | 70 | 4.43 |
| Martino Ruggiero | 3 | 327 | 21.67 |
| David Atienza | 4 | 2219 | 149.60 |