CAREER: Next Generation of High-Level Synthesis for Agile Architectural Design (ArchHLS)

职业：下一代敏捷架构设计高级综合 (ArchHLS)

• 批准号: 2338365
• 负责人: Cong Hao
• 金额: $ 56万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2029-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338365&HistoricalAwards=false
• 关键词: CAREER; Next; Generation; Level; Synthesis

In the landscape of computing, the demand for innovative hardware architectures is ever-growing, driving advancements in computer architecture. However, the conventional Register Transfer Level (RTL) design approach is time-consuming and labor-intensive. This project aims to facilitate the broader adoption of High-Level Synthesis (HLS) tools to significantly reduce design time, particularly for general architectural design. HLS tools enable higher-level programming and automatic synthesis, yet their application in comprehensive computer architecture studies remains limited. The significance of this project lies in promoting agile hardware development by fundamentally innovating HLS tools, overcoming the productivity challenges at the register transfer level, and unlocking the potential for more widespread application of HLS in diverse computing domains. The developed tool chain will be publicly available and exposed to more users by organizing tutorials, workshops, and demo events. The research will be integrated into education programs with activities on research training for undergraduate and master students, including online students, recruitment and retention of students from underrepresented groups, curriculum development, and innovative international design competitions co-hosted with industry.This project aims to revolutionize High-Level Synthesis (HLS) tools by introducing a next-generation tool, ArchHLS, addressing two major research challenges. First, HLS tools are superior in synthesizing a specific algorithm into hardware but have limited capability for general domain-specific architecture designs. Second, it is challenging to design general architectures with compatible compilers and to automatically improve the underlying architecture for evolving workloads. To address these challenges, ArchHLS facilitates agile hardware development by making three key innovations. First, ArchHLS decouples architectural design and workload mapping, allowing flexible architecture extraction and customized control flow. Second, ArchHLS automates architecture evolution to adapt to fast-changing algorithms via automated workload compilation, mapping, and computation pattern matching. Third, ArchHLS enables comprehensive and accurate performance profiling for designs to provide feedback for architecture evolution. Beyond advancing Electronic Design Automation (EDA) tooling, this research has broader societal implications, aligning with the grand vision of sustainability for computing and computing for sustainability, such as climate modeling and scientific computing. The public availability of the toolchain fosters research dissemination, educational integration, and inclusivity efforts, aiming to benefit diverse communities and promote efficient algorithm/architecture co-design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在计算领域，对创新硬件体系结构的需求不断增长，推动了计算机体系结构的进步。然而，传统的寄存器传输级(RTL)设计方法费时费力。该项目旨在促进更广泛地采用高级综合(HLS)工具，以显著缩短设计时间，特别是在一般建筑设计中。HLS工具使更高级别的编程和自动综合成为可能，但它们在全面的计算机体系结构研究中的应用仍然有限。该项目的意义在于通过从根本上创新HLS工具来促进敏捷的硬件开发，克服寄存器传输级别的生产率挑战，并释放HLS在不同计算领域更广泛应用的潜力。开发的工具链将公开提供，并通过组织教程、研讨会和演示活动向更多用户展示。这项研究将被整合到教育项目中，为本科生和硕士学生开展研究培训活动，包括在线学生、从代表性不足的群体中招募和留住学生、课程开发以及与行业联合举办的创新国际设计竞赛。该项目旨在通过引入下一代工具ArchHLS来革新高级综合(HLS)工具，以应对两个主要的研究挑战。首先，HLS工具在将特定算法合成到硬件方面具有优势，但在一般领域特定的体系结构设计中能力有限。其次，使用兼容的编译器设计通用体系结构并自动改进不断变化的工作负载的底层体系结构是具有挑战性的。为了应对这些挑战，ArchHLS通过三项关键创新促进了灵活的硬件开发。首先，ArchHLS分离了体系结构设计和工作负载映射，允许灵活的体系结构提取和定制的控制流。其次，ArchHLS通过自动化的工作负载编译、映射和计算模式匹配，使体系结构演化自动化，以适应快速变化的算法。第三，ArchHLS支持对设计进行全面而准确的性能分析，为架构发展提供反馈。除了先进的电子设计自动化(EDA)工具，这项研究还具有更广泛的社会影响，与计算的可持续性和计算的可持续性的宏伟愿景保持一致，例如气候建模和科学计算。工具链的公开使用促进了研究传播、教育整合和包容性努力，旨在使不同的社区受益，并促进高效的算法/架构联合设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。