用预测引导的随机探索，大幅提升电路合成速度和质量。
The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration
- 用下一状态预测指导随机搜索，迭代优化电路结构。
- 相比顶尖方法，合成速度最高快14倍，电路优化率提升20.94%。
- 适合追求高效电路设计的芯片研发人员使用。
本文提出一种基于MIG的组合数字电路优化新方法，通过引入下一状态预测与迭代选择机制，实现对随机设计空间的高效探索。高质量电路对性能、功耗和成本至关重要，是当前研究热点。该方法在EPFL组合基准测试集上，相比现有最优技术，合成速度最高提升14倍，MIG最小化效果提升达20.94%。同时研究了多种预测模型，发现更高的预测准确率并不一定带来等比例的合成质量或加速提升，表明随机性仍是有效设计中的关键因素。
原文摘要 · Abstract (English)
This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for performance, power, and cost, making this a critical area of active research. Our approach incorporates next-state prediction and iterative selection, significantly accelerating the synthesis process. This novel method achieves up to 14x synthesis speedup and up to 20.94% better MIG minimization on the EPFL Combinational Benchmark Suite compared to state-of-the-art techniques. We further explore various predictor models and show that increased prediction accuracy does not guarantee an equivalent increase in synthesis quality of results or speedup, observing that randomness remains a desirable factor.
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