用于粗粒度并行应用程序的高级综合的自动化流

Vito Giovanni Castellana, Fabrizio Ferrandi
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引用次数: 9

摘要

高级综合(High Level Synthesis, HLS)提供了一种显著提高嵌入式系统设计人员工作效率的方法,它允许从使用(通常是软件)编程语言的高级描述开始自动或半自动地生成硬件加速器。典型的HLS方法构建一个集中的有限状态机(FSM)来控制生成的数据路径,并根据预先确定的静态调度执行操作。然而,基于fsm的方法只能在单个执行流中提取并行性。在存在粗粒度并行性的情况下,以并发函数调用或并行控制结构的形式,它们要么序列化所有操作,要么构建过于复杂的控制器,旨在在单个控制步骤中执行尽可能多的操作(即,它们试图提取尽可能多的指令级并行性)。由此产生的控制器占用过多的面积或导致非常低的工作频率。本文提出了一种支持并行执行和动态调度的加速器HLS方法。该方法利用自适应分布式控制器,该控制器由与每个操作相关的一组通信元素组成。这种控制器设计支持多个并发执行流,从而增加了超越指令级并行性的并行性利用。该方法还支持可变延迟操作,例如内存访问和推测操作。我们将我们的方法应用于一组典型的HLS基准测试,并展示了与传统的基于fsm的流程相比,在有限的面积开销下有价值的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An automated flow for the High Level Synthesis of coarse grained parallel applications
High Level Synthesis (HLS) provides a way to significantly enhance the productivity of embedded system designers, by enabling the automatic or semiautomatic generation of hardware accelerators starting from high level descriptions with (usually software) programming languages. Typical HLS approaches build a centralized Finite State Machine (FSM) to control the generated datapath, performing the operations according to a pre-determined, static schedule. However, FSM-based approaches are only able to extract parallelism within a single execution flow. In the presence of coarse grained parallelism, in the form of concurrent function calls or parallel control structures, they either serialize all the operations, or build excessively complex controllers, aiming at executing as many operation as possible in a single control step (i.e., they try to extract as much instruction level parallelism as possible). The resulting controllers occupy an excessive amount of area or lead to very low operating frequencies. In this paper we propose a methodology for the HLS of accelerators supporting parallel execution and dynamic scheduling. The approach exploits an adaptive distributed controller, composed of a set of communicating elements associated with each operation. This controller design enables supporting multiple concurrent execution flows, thus increasing parallelism exploitation beyond instruction level parallelism. The approach also supports variable latency operations, such as memory accesses and speculative operations. We apply our methodology on a set of typical HLS benchmarks, and demonstrate valuable speed ups with limited area overheads with respect to conventional FSM-based flows.
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