面向高层次综合的深度逆向设计
Deep Inverse Design for High-Level Synthesis
AI总结：
本文提出DID4HLS方法，结合图神经网络与生成模型，通过从HLS后数据中学习条件分布来迭代优化硬件设计，在六个基准上ADRS平均提升42.8%。
摘要：
高层次综合（HLS）显著推进了数字电路设计的自动化，但pragma调优仍需要大量专业知识和时间，这一需求依然具有挑战性。现有的设计空间探索（DSE）解决方案要么采用启发式方法，缺乏用于进一步优化潜力的关键信息；要么采用预测模型，由于HLS耗时较长且设计空间呈指数增长，其泛化能力不足。为应对这些挑战，我们提出了面向HLS的深度逆向设计（DID4HLS），这是一种融合图神经网络与生成模型的新方法。DID4HLS通过从HLS后数据中学习设计特征的条件分布，针对计算密集型算法迭代优化硬件设计。与四个最先进的DSE基线相比，我们的方法在六个基准测试上，相较于表现最佳的基线，在平均参考集距离（ADRS）上平均提升了42.8%，同时展现出较高的鲁棒性和效率。代码可在https://github.com/PingChang818/DID4HLS获取。
英文摘要：
High-level synthesis (HLS) has significantly advanced the automation of digital circuits design, yet the need for expertise and time in pragma tuning remains challenging. Existing solutions for the design space exploration (DSE) adopt either heuristic methods, lacking essential information for further optimization potential, or predictive models, missing sufficient generalization due to the time-consuming nature of HLS and the exponential growth of the design space. To address these challenges, we propose Deep Inverse Design for HLS (DID4HLS), a novel approach that integrates graph neural networks and generative models. DID4HLS iteratively optimizes hardware designs aimed at compute-intensive algorithms by learning conditional distributions of design features from post-HLS data. Compared to four state-of-the-art DSE baselines, our method achieved an average improvement of 42.8% on average distance to reference set (ADRS) compared to the best-performing baselines across six benchmarks, while demonstrating high robustness and efficiency. The code is available at https://github.com/PingChang818/DID4HLS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
