Flexible Hardware Accelerator Design Generation with Spiral

Guanglin Xu, J. Hoe, F. Franchetti
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Abstract

Hardware specialization has become a widely employed technique for approaching higher performance and en-ergy efficiency in computer systems. Yet obtaining efficient cus-tom hardware designs remains a challenging and tedious task, calling for the automated approaches. In the past, Spiral has been used for generating high-throughput streaming hardware designs for linear transform kernels. This paper is motivated by an observation that a memory-based iterative computing model may allow us to trade off throughput for algorithmic flexibility. In this paper, we present a hardware generation approach that generates and optimizes algorithms using Spiral's multi-level domain-specific languages (DSLs), targeting a scalar load-store architecture. We have incorporated this approach as a hardware backend into the Spiral system. Our evaluation of this approach on several fundamental kernels shows flexibility with reasonable performance and resource utilization.
柔性硬件加速器设计与螺旋生成
硬件专门化已经成为一种广泛采用的技术,以接近更高的性能和能源效率的计算机系统。然而,获得高效的定制硬件设计仍然是一项具有挑战性和繁琐的任务,需要自动化的方法。在过去,螺旋已被用于为线性变换内核生成高吞吐量的流硬件设计。本文的动机是观察到基于内存的迭代计算模型可能允许我们为了算法的灵活性而权衡吞吐量。在本文中,我们提出了一种硬件生成方法,该方法使用螺旋的多级领域特定语言(dsl)生成和优化算法,目标是标量负载存储体系结构。我们已经将这种方法作为硬件后端整合到Spiral系统中。我们在几个基本内核上对这种方法进行了评估,显示出具有合理性能和资源利用率的灵活性。
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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