Name
Title | ||
|---|---|---|
A Learning-Based Recommender System for Autotuning Design Flows of Industrial High-Performance Processors |
Abstract | ||
|---|---|---|
Logic synthesis and physical design (LSPD) tools automate complex design tasks previously performed by human designers. One time-consuming task that remains manual is configuring the LSPD flow parameters, which significantly impacts design results. To reduce the parameter-tuning effort, we propose an LSPD parameter recommender system that involves learning a collaborative prediction model through tensor decomposition and regression. Using a model trained with archived data from multiple state-of-the-art 14nm processors, we reduce the exploration cost while achieving comparable design quality. Furthermore, we demonstrate the transfer-learning properties of our approach by showing that this model can be successfully applied for 7nm designs.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3316781.3323919 | Proceedings of the 56th Annual Design Automation Conference 2019 |
DocType | ISBN | Citations |
Conference | 978-1-4503-6725-7 | 1 |
PageRank | References | Authors |
0.38 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jihye kwon | 1 | 12 | 3.65 |
| MATTHEW M. ZIEGLER | 2 | 219 | 50.73 |
| L. P. Carloni | 3 | 61 | 9.98 |