eda

SwiftCTS is a physics-informed surrogate framework that uses gradient-boosted ensembles and few-shot calibration to rapidly predict and Pareto-optimize clock tree metrics (power, wirelength, timing skew) across unseen designs, achieving high accuracy with minimal training data.

Alpha-RTL (TTT-RTL) introduces a test-time training framework for RTL hardware optimization, using reinforcement learning with EDA feedback to refine LLM-generated designs. It achieves significant PPA reductions on benchmarks.

RTL-BenchMT is an agentic framework that automatically identifies and revises flawed cases and detects overfitting in RTL generation benchmarks, reducing human maintenance effort in EDA research.

NVIDIA researchers present the first self-evolving logic synthesis framework where multi-agent LLMs autonomously refine the ABC EDA tool codebase.