Unleashing the Potential of AQFP Logic Placement via Entanglement Entropy and Projection
Yinuo Bai, Enxin Yi, Wei W. Xing, Bei Yu, Zhou Jin
摘要
Adiabatic quantum-flux-parametron (AQFP) logic, known for its energy efficiency, has emerged as a prominent superconductor-based logic family, surpassing traditional rapid single flux quantum (RSFQ) logic. In AQFP circuits, each cell operates on AC power, serving as both a power supply and clock signal to drive data flow across clock phases. However, signal attenuation with increasing wirelength may result in more potential data errors. To address this, rows of buffers are inserted as repeaters to ensure data synchronization and avoid wirelength violations. However, these inserted buffer rows in the AQFP placement significantly amplifies power consumption and circuit delay. To address these challenges, in this paper, we propose an innovative and analytical method for the placement of AQFP. The proposed method aims at minimizing the need for additional buffers. The framework incorporates two key features: (1) entanglement entropy for topology initialization and (2) projection for placement and buffering. These features offer advantages such as avoiding intensive computations, including fix-order Lagrangian optimization in large-scale scenarios, while significantly reducing the required number of buffer rows. The experimental results validate the efficiency of the proposed framework, demonstrating an average reduction of 81% in the required number of buffers and acceleration of 1.88x in the processing time compared with the state-of-the-art method.
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- Optimizing quantum circuit placement via machine learningHongxiang Fan, Ce Guo, Wayne LukDAC 2022 · 被引用 30 次
- Beyond local optimality of buffer and splitter insertion for AQFP circuitsSiang-Yun Lee, Heinz Riener, Giovanni De MicheliDAC 2022 · 被引用 21 次
- TAAS: a timing-aware analytical strategy for AQFP-capable placement automationPeiyan Dong, Yanyue Xie, Hongjia Li, Mengshu Sun 等DAC 2022 · 被引用 7 次
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