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iPO: Constant Liar Parameter Optimization for Placement with Representation and Transfer Learning
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DOI：10.1145/3747292.png)
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Abstract
En 中文
Placement is a critical and time-consuming step in very-large-scale integration (VLSI) design flow. As placement methods continue to be researched, they introduce more parameters, making current methods for configuring parameters heavily reliant on human experience for each design. This article proposes a novel cross-design parameter optimization method, iPO, to accelerate parameter tuning without human involvement in different placement engines (like iEDA-iPL and DREAMPlace). Specifically, we introduce a heuristic strategy called Constant Liar to accelerate parameter tuning, allowing us to optimize parameters concurrently on different machines. Our research indicates that optimizing parameters for every design is time-consuming. To address the inefficiency of parameter tuning, we propose a cross-design parameter transfer learning strategy. This strategy measures the cosine similarity between designs in collaboration with a graph embedding algorithm representing netlists and cells. Compared with DREAMPlace on ISPD2015 benchmarks, our method achieves average improvements of 9.8% in half-perimeter wirelength (HPWL) and 12.0% in route congestion. When compared with AutoDMP, iPO shows an average improvement of 11% in HPWL and 12.3% in congestion, along with a 3.49 & times; speed-up in the number of search iterations. Furthermore, we extended our experiments to the iEDA-28nm benchmarks, showing average improvements of 4.7%, 2.7% and 2.8% in HPWL, worst negative slack (WNS) and total negative slack (TNS), respectively, compared with iEDA-iPL. Finally, our ablation studies on parallelization demonstrate that using 10 parallel processes results in approximately an 18 & times; speed-up compared with using a single process.
Keywords:
AI/ML
VLSI Placement
design space exploration
transfer learning
parallelization
representation learning
Journal
A
IF:
2
Papers:
112
Citations:
1.2K