SODA-OPT an MLIR based flow for co-design and high-level synthesis
SODA-OPT an MLIR based flow for co-design and high-level synthesis
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SODA-OPT 基于 MLIR 的协同设计和高级综合流程
DOI：
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发表时间：
2022
期刊：
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通讯作者：
Antonino Tumeo
中科院分区：
文献类型：
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作者：
Nicolas Bohm Agostini;S. Curzel;D. Kaeli;Antonino Tumeo
由于技术和功率的限制，通用处理单元的性能提升越来越小。计算机体系结构创新对于保持性能稳定增长至关重要。因此，特定领域的加速器正在重新受到关注，并已证明有利于不同的科学和机器学习应用[1，3]。高级综合（HLS）提供了一种从高级应用程序开始快速生成特定领域加速器硬件描述的方法。然而，最先进的工具通常需要将应用程序手动转换为C/C++并仔细注释，以提高最终设计性能。这个繁琐的过程阻碍了科学家和研究人员利用HLS的力量，因为他们的许多应用程序需要大量的工作才能移植。
Due to technology and power limitations, general-purpose processing units are experiencing progressively smaller performance gains. Computer architecture innovations are essential to keep performance steadily increasing. Thus domain-specific accelerators are receiving renewed interest and have shown to benefit different scientific and machine learning applications [1, 3]. High-Level-Synthesis (HLS) provides a way to quickly generate hardware descriptions for domain-specific accelerators starting from high-level applications. However, state-of-the-art tools typically require the application to be manually translated to C/C++ and carefully annotated to improve final design performance. This cumbersome process prevents scientists and researchers from tapping into the power of HLS, as many of their applications require significant effort to be ported.