SwiftCTS: Machine Learning Framework Accelerates Clock Tree Synthesis in Chip Design
Researchers have introduced SwiftCTS, a machine learning surrogate framework that dramatically accelerates Clock Tree Synthesis (CTS) optimization in chip physical design by combining physics-informed features with gradient-boosted ensembles. Unlike existing ML approaches that require expensive retraining for new chip architectures, SwiftCTS uses a K-shot multiplicative calibration mechanism requiring only one or two physical reference runs to adapt to unseen designs. The system can evaluate 100,000 CTS configurations in under ten seconds on a CPU, potentially reducing a major bottleneck in semiconductor design workflows.
Clock Tree Synthesis is a computationally intensive step in chip physical design that requires iterative invocations of EDA tools to optimize power consumption, wirelength, and timing skew across a vast configuration space. SwiftCTS addresses two key limitations of prior ML-based approaches: the need for expensive retraining when encountering new macro architectures, and architectural mismatch with the millions of evaluations required by exhaustive combinatorial search. The framework pairs lightweight, physics-grounded statistical features with gradient-boosted ensemble models, enabling training in under five seconds on a CPU and sub-millisecond inference without GPU hardware. Its K-shot calibration mechanism anchors predictions to just one or two physical reference runs, reducing power prediction error from 24.5% to 3.3% and wirelength error from 56.6% to under 1% on out-of-distribution designs. Integrated with an evolutionary optimizer, the system evaluates 100,000 configurations in under ten seconds and produces Pareto-optimal frontiers validated within the OpenROAD open-source EDA flow. Closed-loop validation demonstrated prediction errors below 0.5% for power and wirelength, and timing skew predictions within five picoseconds on an out-of-distribution benchmark, consistently outperforming default tool heuristics. The code has been made publicly available, and the work is currently a preprint pending peer review.
What's missing
The study has not yet undergone formal peer review, as it is an arXiv preprint. Comparisons are made against default EDA tool heuristics, but no direct benchmarking against other state-of-the-art ML-based CTS optimization methods is described in the abstract, leaving the relative advantage over competing ML approaches unclear. The generalizability of the K-shot calibration mechanism to highly diverse or proprietary chip architectures beyond the tested benchmarks remains an open question.
What different sources said
- arXiv cs.LGCenter
SwiftCTS: Fast Cross-Design Prediction and Pareto Optimization of Clock Tree Metrics via Few-Shot Calibration
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