DeepCell fuses AIG and post-mapping netlist views with masked autoencoding, achieving 2.77% lower ECO patch cost and 15-16% lower area-delay product in technology mapping.
Practical Boolean Decomposition for Delay-driven LUT Mapping
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abstract
Ashenhurst-Curtis decomposition (ACD) is a decomposition technique used, in particular, to map combinational logic into lookup tables (LUTs) structures when synthesizing hardware designs. However, available implementations of ACD suffer from excessive complexity, search-space restrictions, and slow run time, which limit their applicability and scalability. This paper presents a novel fast and versatile technique of ACD suitable for delay optimization. We use this new formulation to compute two-level decompositions into a variable number of LUTs and enhance delay-driven LUT mapping by performing ACD on the fly. Compared to state-of-the-art technology mapping, experiments on heavily optimized benchmarks demonstrate an average delay improvement of 12.39%, and area reduction of 2.20% with affordable run time. Additionally, our method improves 4 of the best delay results in the EPFL synthesis competition without employing design-space exploration techniques.
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DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning
DeepCell fuses AIG and post-mapping netlist views with masked autoencoding, achieving 2.77% lower ECO patch cost and 15-16% lower area-delay product in technology mapping.