SmaRTLy: RTL Optimization with Logic Inferencing and Structural Rebuilding

This paper proposes smaRTLy: a new optimization technique for multiplexers in Register-Transfer Level (RTL) logic synthesis. Multiplexer trees are very common in RTL designs, and traditional tools like Yosys optimize them by traversing the tree and monitoring control port values. However, this method does not fully exploit the intrinsic logical relationships among signals or the potential for structural optimization. To address these limitations, we develop innovative strategies to remove redundant multiplexer trees and restructure the remaining ones, significantly reducing the overall gate count. We evaluate smaRTLy on the IWLS-2005 and RISC-V benchmarks, achieving an additional 8.95% reduction in AIG area compared to Yosys. We also evaluate smaRTLy on an industrial benchmark in the scale of millions of gates, results show that smaRTLy can remove 47.2% more AIG area than Yosys. These results demonstrate the effectiveness of our logic inferencing and structural rebuilding techniques in enhancing the RTL optimization process, leading to more efficient hardware designs.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133131
Primary Topic
Formal Methods in Verification
Type
article
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article

SmaRTLy: RTL Optimization with Logic Inferencing and Structural Rebuilding

Yang Sun, Evangeline F. Y. Young, Chengxi Li, Mingxuan Yuan et al.
Formal Methods in Verification
article

SmaRTLy: RTL Optimization with Logic Inferencing and Structural Rebuilding

Yang Sun, Evangeline F. Y. Young, Chengxi Li, Mingxuan Yuan, Yiwen Wang, Lei Chen
article en

Abstract

This paper proposes smaRTLy: a new optimization technique for multiplexers in Register-Transfer Level (RTL) logic synthesis. Multiplexer trees are very common in RTL designs, and traditional tools like Yosys optimize them by traversing the tree and monitoring control port values. However, this method does not fully exploit the intrinsic logical relationships among signals or the potential for structural optimization. To address these limitations, we develop innovative strategies to remove redundant multiplexer trees and restructure the remaining ones, significantly reducing the overall gate count. We evaluate smaRTLy on the IWLS-2005 and RISC-V benchmarks, achieving an additional 8.95% reduction in AIG area compared to Yosys. We also evaluate smaRTLy on an industrial benchmark in the scale of millions of gates, results show that smaRTLy can remove 47.2% more AIG area than Yosys. These results demonstrate the effectiveness of our logic inferencing and structural rebuilding techniques in enhancing the RTL optimization process, leading to more efficient hardware designs.

Chinese University of Hong Kong (HK), Huawei Technologies (Sweden) (SE)
Openalex Percentile: Top 74%
Formal Methods in Verification
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