Wire sorts: a language abstraction for safe hardware composition

Michael Christensen, T. Sherwood, Jonathan Balkind, B. Hardekopf
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引用次数: 8

Abstract

Effective digital hardware design fundamentally requires decomposing a design into a set of interconnected modules, each a distinct unit of computation and state. However, naively connecting hardware modules leads to real-world pathological cases which are surprisingly far from obvious when looking at the interfaces alone and which are very difficult to debug after synthesis. We show for the first time that it is possible to soundly abstract even complex combinational dependencies of arbitrary hardware modules through the assignment of IO ports to one of four new sorts which we call: to-sync, to-port, from-sync, and from-port. This new taxonomy, and the reasoning it enables, facilitates modularity by escalating problematic aspects of module input/output interaction to the language-level interface specification. We formalize and prove the soundness of our new wire sorts, implement them in a practical hardware description language, and demonstrate they can be applied and even inferred automatically at scale. Through an examination of the BaseJump STL, the OpenPiton manycore research platform, and a complete RISC-V implementation, we find that even on our biggest design containing 1.5 million primitive gates, analysis takes less than 31 seconds; that across 172 unique modules analyzed, the inferred sorts are widely distributed across our taxonomy; and that by using wire sorts, our tool is 2.6–33.9x faster at finding loops than standard synthesis-time cycle detection.
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线排序:一种用于安全硬件组合的语言抽象
有效的数字硬件设计从根本上需要将设计分解为一组相互连接的模块,每个模块都有不同的计算和状态单元。然而,天真地连接硬件模块会导致现实世界的病态情况,这些情况在单独查看接口时远不明显,并且在综合后很难调试。我们首次展示了通过将IO端口分配给我们称之为同步、端口、从同步和端口的四种新类型之一,可以很好地抽象任意硬件模块的复杂组合依赖关系。这种新的分类法及其支持的推理通过将模块输入/输出交互的问题方面升级到语言级接口规范来促进模块化。我们将形式化并证明我们的新线分类的合理性,用实用的硬件描述语言实现它们,并演示它们可以大规模地应用甚至自动推断。通过对BaseJump STL, OpenPiton多核研究平台和完整的RISC-V实现的检查,我们发现即使在包含150万个原始门的最大设计上,分析时间也不到31秒;在分析的172个独特模块中,推断的排序广泛分布在我们的分类法中;通过使用线排序,我们的工具在查找循环方面比标准的合成时间周期检测快2.6 - 33.9倍。
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