BioElectronics (United States)

The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.

The work proposes PrefixAgent, a two-phase large-language-model-driven framework for prefix adder optimization: in Phase I a fine-tuned large reasoning model iteratively optimizes the backbone via regroup tool calls, and in Phase II it performs local timing repair with level-opt, fanout-opt, and node clone tools; the authors use e-graph equality saturation and explanation to generate interpretable rewrite trajectories as supervision data, and under the NanGate45 and OpenROAD flow PrefixAgent produces smaller-area adders than DP, MCTS, PrefixRL, CircuitVAE, and PrefixGPT in nearly all configurations, achieving up to 11.3% area reduction over the best baseline at 64 bits and also improving area in a commercial flow and a 256-PE systolic array.