Late Breaking Results: Multi-Objective Multi-Bit Flip-Flop Placement Considering Pre-Placed Cells

Clustering single-bit flip-flops (SBFFs) into multi-bit flip-flops (MBFFs) effectively reduces power and area. However, excessive displacement during the clustering and legalization process may incur significant timing degradation. To address this issue, we propose the first comprehensive MBFF placement methodology that addresses excessive displacement caused by pre-placed cells during clustering and legalization while simultaneously optimizing timing, power, area, and bin utilization. Our methodology includes three main features: (1) a force model to relocate flip-flops and reduce timing violations, (2) a clustering and legalization process to reduce timing degradation caused by displacement, and (3) a multi-objective function to identify flip-flop candidates suitable for MBFF clustering. Our methodology outperforms all participating teams in the 2024 CAD Contest at ICCAD on Power and Timing Optimization Using Multi-Bit Flip-Flops, based on exactly the same settings.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133043
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
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article

Late Breaking Results: Multi-Objective Multi-Bit Flip-Flop Placement Considering Pre-Placed Cells

Cheng-Yen Li, Yao‐Wen Chang, Zheng‐Wei Chen, Shyh‐Chyan Chen et al.
Neuroscience and Neural Engineering
article

Late Breaking Results: Multi-Objective Multi-Bit Flip-Flop Placement Considering Pre-Placed Cells

Cheng-Yen Li, Yao‐Wen Chang, Zheng‐Wei Chen, Shyh‐Chyan Chen, Chenghao Su
article en

Abstract

Clustering single-bit flip-flops (SBFFs) into multi-bit flip-flops (MBFFs) effectively reduces power and area. However, excessive displacement during the clustering and legalization process may incur significant timing degradation. To address this issue, we propose the first comprehensive MBFF placement methodology that addresses excessive displacement caused by pre-placed cells during clustering and legalization while simultaneously optimizing timing, power, area, and bin utilization. Our methodology includes three main features: (1) a force model to relocate flip-flops and reduce timing violations, (2) a clustering and legalization process to reduce timing degradation caused by displacement, and (3) a multi-objective function to identify flip-flop candidates suitable for MBFF clustering. Our methodology outperforms all participating teams in the 2024 CAD Contest at ICCAD on Power and Timing Optimization Using Multi-Bit Flip-Flops, based on exactly the same settings.

National Taiwan University of Science and Technology (TW), National Taiwan University (TW)
Openalex Percentile: Top 74%
Neuroscience and Neural Engineering
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