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Scalable Sequential Optimization Under Observability Don't Cares

2023/11/16 by Dewmini Sudara Marakkalage, Marakkalage, Dewmini Sudara, Eleonora Testa +9
Computer Science · Engineering · #FOS: Computer and information sciences #Formal Methods in Verification #Logic in Computer Science (cs.LO) #Radiation Effects in Electronics #VLSI and Analog Circuit Testing

paper · pdf · doi:10.48550/arxiv.2311.09967

openalex publication_date 2023/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Sequential logic synthesis can provide better Power-Performance-Area (PPA) than combinational logic synthesis since it explores a larger solution space. As the gate cost in advanced technologies keeps rising, sequential logic synthesis provides a powerful alternative that is gaining momentum in the EDA community. In this work, we present a new scalable algorithm for don't-care-based sequential logic synthesis. Our new approach is based on sequential k-step induction and can apply both redundancy removal and resubstitution transformations under Sequential Observability Don't Cares (SODCs). Using SODC-based optimizations with induction is a challenging problem due to dependencies and alignment of don't cares among the base case and the inductive case. We propose a new approach utilizing the full power of SODCs without limiting the solution space. Our algorithm is implemented as part of an industrial tool and achieves 6.9% average area improvement after technology mapping when compared to state-of-the-art sequential synthesis methods. Moreover, all the new sequential optimizations can be verified using state-of-the-art sequential verification tools.

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