SOFA-H: Post-Synthesis Area Optimization via Functionally Encoded, Net-Driven Subgraph Mining and SAT-Based Hypercell Remapping

Synthesized netlists often leave substantial room for area optimization due to the limited function diversity in standard cell libraries, which frequently results in recurring logic patterns that could be compacted through cell combination-referred to as hypercells in this work. While prior studies have demonstrated the potential of hypercell-based optimization, most lack efficient and scalable mining strategies. We present SOFA-H, a post-synthesis framework that extracts and remaps hypercells for maximum area reduction. SOFA-H (i) mines fanout-induced subgraphs and canonically encodes them using P-Representatives, (ii) selects an optimal set of hypercells with non-overlapping replacements via a one-shot weighted MaxSAT formulation, and (iii) supports high input, multi-output cells with scalable runtime. Evaluated on the EPFL benchmark suite synthesized using FreePDK45 and ASAP7, SOFA-H achieves average area reductions of 12.2% and 7.4%, respectively, and runs $380 \times$ faster on average at ASAP7 compared to the state-of-the-art method. These results demonstrate that the extracted hypercells offer a scalable and effective path to closing the area gap left by conventional synthesis.