A Case for Precise, Fine-Grained Pointer Synthesis in High-Level Synthesis

Nadesh Ramanathan, G. Constantinides, John Wickerson
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Abstract

This article combines two practical approaches to improve pointer synthesis within HLS tools. Both approaches focus on inefficiencies in how HLS tools treat the points-to graph—a mapping that connects each instruction to the memory locations that it might access at runtime. HLS pointer synthesis first computes the points-to graph via pointer analysis and then implements its connections in hardware, which gives rise to two inefficiencies. First, HLS tools typically favour pointer analysis that is fast, sacrificing precision. Second, they also favour centralising memory connections in hardware for instructions that can point to more than one location. In this article, we demonstrate that a more precise pointer analysis coupled with decentralised memory connections in hardware can substantially reduce the unnecessary sharing of memory resources. We implement both flow- and context-sensitive pointer analysis and fine-grained memory connections in two modern HLS tools, LegUp and Vitis HLS. An evaluation on three benchmark suites, ranging from non-trivial pointer use to standard HLS benchmarks, indicates that when we improve both precision and granularity of pointer synthesis, on average, we can reduce area and latency by around 42% and 37%, respectively.
高级综合中精确、细粒度指针综合的一个例子
本文结合了两种实用的方法来改进HLS工具中的指针合成。这两种方法都关注于HLS工具处理点到图(point -to - graph)的低效问题,点到图是将每条指令连接到运行时可能访问的内存位置的映射。HLS指针综合首先通过指针分析计算点到图,然后在硬件上实现它的连接,这造成了两个低效率。首先,HLS工具通常倾向于快速的指针分析,牺牲了精度。其次,他们还倾向于在硬件中集中存储连接,以用于可以指向多个位置的指令。在本文中,我们演示了更精确的指针分析与硬件中的分散内存连接相结合可以大大减少不必要的内存资源共享。我们在两个现代HLS工具LegUp和Vitis HLS中实现了流和上下文敏感的指针分析和细粒度的内存连接。对三个基准套件(从非平凡指针使用到标准HLS基准)的评估表明,当我们提高指针合成的精度和粒度时,平均而言,我们可以分别减少42%和37%的面积和延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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