Recent years have seen increasing employment of decision intelligence in electronic design automation (EDA), which aims to reduce the manual efforts and boost the design closure process in modern toolflows. However, existing approaches either require a large number of labeled data and expensive training efforts, or are limited in practical EDA toolflow integration due to computation overhead. This paper presents a generic end-to-end sequential decision making framework FlowTune for synthesis tooflow optimization, with a novel high-performance domain-specific, multi-stage multi-armed bandit (MAB) approach. This framework addresses optimization problems on Boolean optimization problems such as a) And-Inv-Graphs (# nodes), b) Conjunction Normal Form (CNF) minimization (# clauses) for Boolean Satisfiability; logic synthesis and technology mapping problems such as c) post static timing analysis (STA) delay and area optimization for standard-cell technology mapping, and d) FPGA technology mapping for 6-in LUT architectures. Moreover, we demonstrate the high extnsibility and generalizability of the proposed domain-specific MAB approach with end-to-end FPGA design flow, evaluated at post-routing stage, with two different FPGA backend tools (OpenFPGA and VPR) and two different logic synthesis representations (AIGs and MIGs). FlowTune is fully integrated with ABC [1], Yosys [2], VTR [3], LSOracle [4], OpenFPGA [5], and industrial tools, and is released publicly. The experimental results conducted on various design stages in the flow all demonstrate that our framework outperforms both hand-crafted flows [1] and ML explored flows [6], [7] in quality of results, and is orders of magnitude faster compared to ML-based approaches.

翻译：近年来，决策智能在电子设计自动化（EDA）中的应用日益增多，旨在减少人工投入并加速现代工具流程中的设计收敛过程。然而，现有方法要么需要大量标注数据和昂贵的训练成本，要么因计算开销过大而在实际EDA工具流集成中受到限制。本文提出了一种通用的端到端序列决策框架FlowTune，用于综合工具流优化，该框架采用了一种新颖的高性能领域特定多阶段多臂老虎机（MAB）方法。该框架解决了布尔优化问题，包括：a) 与非图（#节点）、b) 针对布尔可满足性的合取范式（CNF）最小化（#子句）；以及逻辑综合与技术映射问题，如：c) 针对标准单元技术映射的静态时序分析（STA）后延迟与面积优化，以及d) 面向6输入LUT架构的FPGA技术映射。此外，我们通过端到端FPGA设计流程（在后布线阶段评估），展示了所提出的领域特定MAB方法的高度可扩展性和泛化能力，该流程使用了两种不同的FPGA后端工具（OpenFPGA和VPR）及两种逻辑综合表示（AIG和MIG）。FlowTun全面集成了ABC [1]、Yosys [2]、VTR [3]、LSOracle [4]、OpenFPGA [5]以及工业工具，并已公开发布。在流程各设计阶段进行的实验结果表明，我们的框架在结果质量上优于人工定制流程 [1] 和机器学习探索流程 [6]、[7]，且与基于ML的方法相比，速度提升了数个数量级。