AI-Driven, Timing-Aware Power Optimization
Post-route power recovery changes cell choices under tightly coupled timing, electrical and physical constraints, and the value of each change depends on what it costs to repair its side effects. We propose an AI-driven framework in which a learned model drives both phases of the optimization: it ranks the powerrecovery edits to try and the repair actions that follow, while deterministic electronic design automation (EDA) analyses govern every acceptance. The framework records baseline constraint margins, identifies the damage a recovery transaction introduces in each analysis view, holds pre-existing violations at their baseline rather than working them inside the power step, and caps re...
| Authors | Amit Sharma |
|---|---|
| Primary category | cs.AR |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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- 16 Oct 2026 Description: Post-route power recovery changes cell choices under tightly coupled timing, electrical and physical constraints, and the value of each change depends on what it costs to repair its side effects. We propose an AI-driven framework in which a → Post-route power recovery changes cell choices under tightly coupled timing, electrical and physical constraints, and the value of each change depends on what it costs to repair its side effects. We propose an AI-driven framework in which a
- 16 Oct 2026 Topics added cs.AR, cs.LG removed hf-daily-papers, gradient-boosted models, ensemble, power recovery, electronic design automation (eda), timing-context features, constraint-regression risk, acquisition scores, physical implementation
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