The 3-D integration helps improve performance and density of electronic systems. However, since electrical and thermal performance for 3-D integration is related to each other, their codesign is required. Machine learning, a promising approach in artificial intelligence, has recently shown promise for addressing engineering optimization problems. In this paper, we apply machine learning for the optimization of 3-D integrated systems where the electrical performance and thermal performance need to be analyzed together for maximizing performance. In such systems, modeling can be challenging due to the multiscale geometries involved, which increases computation time per iteration. In this paper, we show that machine learning can be applied to such systems where multiple parameters can be optimized to achieve the desired performance using the minimum number of iterations. These results have been compared with other promising optimization methods in this paper. The results show that on an average, 4.4%, 31.1%, and 6.9% improvement in temperature gradient, CPU time, and skew are possible using machine learning, as compared with other methods.
|Original language||English (US)|
|Number of pages||10|
|Journal||IEEE Transactions on Very Large Scale Integration (VLSI) Systems|
|State||Published - Jun 2017|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Electrical and Electronic Engineering