Name
Title | ||
|---|---|---|
Adaptive Low-Rank Tensor Approximation for SRAM Yield Analysis using Bootstrap Resampling |
Abstract | ||
|---|---|---|
With increasing design complexity and reliability requirement, performance failure has become a main threat for a robust circuit design. The major challenge to develop a high-sigma yield analysis method is “Curse of Dimensionality”. In this paper, we propose a novel Adaptive Low-Rank Tensor Approximation (Adaptive LRTA) method. It starts with a Latin Hypercube Sampling set to build the initial LRTA meta-model. Our adaptive framework has multiple iterations of meta-model refinement. In each iteration, we evaluate the confidence interval by constructing a set of bootstrap resampled LRTA meta-models, and develop a learning function which determines our experimental design enrichment criteria. The experiment results on high-dimensional SRAM column validate that our method has better efficiency and higher accuracy. Our adaptive LRTA uses 2-6X fewer samples than state-of-the-art methods without compromising accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ASICON47005.2019.8983442 | 2019 IEEE 13th International Conference on ASIC (ASICON) |
Keywords | Field | DocType |
Process Variation,Failure Probability,Low-Rank Tensor Approximation,Bootstrap Resampling | Tensor,Computer science,Circuit design,Algorithm,Curse of dimensionality,Real-time computing,Bootstrapping (statistics),Static random-access memory,Process variation,Latin hypercube sampling,Bootstrapping (electronics) | Conference |
ISSN | ISBN | Citations |
2162-7541 | 978-1-7281-0736-3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shi Xiao | 1 | 9 | 5.97 |
| Jinlong Yan | 2 | 0 | 0.34 |
| Hao Yan | 3 | 12 | 8.71 |
| Jiajia Zhang | 4 | 0 | 0.34 |
| Jinxin Wang | 5 | 0 | 0.34 |
| Longxing Shi | 6 | 116 | 39.08 |
| Lei He | 7 | 1015 | 86.74 |