Dynamically Scheduled High-level Synthesis

Lana Josipović, Radhika Ghosal, P. Ienne
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引用次数: 72

Abstract

High-level synthesis (HLS) tools almost universally generate statically scheduled datapaths. Static scheduling implies that circuits out of HLS tools have a hard time exploiting parallelism in code with potential memory dependencies, with control-dependent dependencies in inner loops, or where performance is limited by long latency control decisions. The situation is essentially the same as in computer architecture between Very-Long Instruction Word (VLIW) processors and dynamically scheduled superscalar processors; the former display the best performance per cost in highly regular embedded applications, but general purpose, irregular, and control-dominated computing tasks require the runtime flexibility of dynamic scheduling. In this work, we show that high-level synthesis of dynamically scheduled circuits is perfectly feasible by describing the implementation of a prototype synthesizer which generates a particular form of latency-insensitive synchronous circuits. Compared to a commercial HLS tool, the result is a different trade-off between performance and circuit complexity, much as superscalar processors represent a different trade-off compared to VLIW processors: in demanding applications, the performance is very significantly improved at an affordable cost. We here demonstrate only the first steps towards more performant high-level synthesis tools adapted to emerging FPGA applications and the demands of computing in broader application domains.
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动态安排的高级综合
高级合成(HLS)工具几乎普遍生成静态调度的数据路径。静态调度意味着HLS工具之外的电路很难利用代码中的并行性,这些代码具有潜在的内存依赖关系、内部循环中的控件依赖关系,或者性能受到长延迟控制决策的限制。这种情况与计算机体系结构中超长指令字(VLIW)处理器和动态调度的超标量处理器之间的情况基本相同;前者在高度规则的嵌入式应用程序中显示出最佳的每成本性能,但一般用途、不规则和控制主导的计算任务需要动态调度的运行时灵活性。在这项工作中,我们通过描述生成特定形式的延迟不敏感同步电路的原型合成器的实现,表明动态调度电路的高级综合是完全可行的。与商业HLS工具相比,结果是性能和电路复杂性之间的不同权衡,就像标量处理器与VLIW处理器相比代表了不同的权衡一样:在要求苛刻的应用程序中,性能以可承受的成本得到了显着提高。我们在这里只展示了迈向高性能高级合成工具的第一步,这些工具适合新兴的FPGA应用和更广泛应用领域的计算需求。
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Architecture and Circuit Design of an All-Spintronic FPGA Session details: Session 6: High Level Synthesis 2 A FPGA Friendly Approximate Computing Framework with Hybrid Neural Networks: (Abstract Only) Software/Hardware Co-design for Multichannel Scheduling in IEEE 802.11p MLME: (Abstract Only) Session details: Special Session: Deep Learning
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