SwiftCTS: Fast Cross-Design Prediction and Pareto Optimization of Clock Tree Metrics via Few-Shot Calibration
arXiv:2606.11348v1 Announce Type: new Abstract: Clock Tree Synthesis (CTS) is a computationally expensive stage in the physical design flow, requiring iterative EDA tool invocations to navigate a vast configuration space for optimal power, wirelength, and timing skew. Existing machine learning approaches require computationally expensive retraining or fine-tuning cycles to adapt to unseen macro architectures and are architecturally mismatched to the millions of evaluations demanded by exhaustive combinatorial search. We present SwiftCTS, a physics-informed surrogate framework that addresses bo
The increasing complexity of chip designs and the computational burden of physical design stages like Clock Tree Synthesis are driving the need for more efficient optimization methods.
Improving the efficiency of chip design processes directly impacts the speed and cost of developing advanced computing hardware, which is critical for AI and other high-tech sectors.
The introduction of physics-informed surrogate models like SwiftCTS offers a potential path to significantly reduce the computational cost and time required for chip design optimization, making advanced designs more accessible and faster to produce.
- · EDA tool developers
- · Semiconductor companies
- · AI hardware manufacturers
- · High-performance computing sector
- · Traditional EDA methodologies
- · Companies with suboptimal design efficiency
Faster and more cost-effective development of complex semiconductor chips.
Accelerated innovation in AI hardware and other advanced computing domains due to quicker design cycles.
Potential for smaller companies to compete more effectively in chip design by leveraging more efficient tools, democratizing advanced silicon development.
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Read at arXiv cs.LG