在CGRAs上实现和评估配置洗涤:一个案例研究

Syed M. A. H. Jafri, S. Piestrak, A. Hemani, K. Paul, J. Plosila, H. Tenhunen
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引用次数: 2

摘要

本文研究了容错粗粒度可重构阵列(CGRAs)中使用的各种配置清洗技术所带来的开销。如今,可重构架构承载了大量配置内存。随着我们在纳米领域的进一步发展,这些构型记忆越来越容易受到单一事件的干扰,例如由宇宙辐射引起的干扰。配置清理是一种常用的技术,用于保护这些配置内存不受单个事件干扰。现有的配置清洗工作只处理FPGA,没有任何参考CGRAs(其中配置存储器消耗高达50%的硅面积)。此外,在已知的文献中,缺乏对各种构型洗涤技术的全面比较,以指导系统设计人员了解可应用于CGRAs的不同洗涤方法的优缺点。为了解决这些问题,在本文中,我们对各种配置清洗技术进行了分类,并量化了它们在CGRA上实现时的权衡。合成结果表明,擦洗逻辑产生的硅开销可以忽略不计(最多占计算单元面积的3%)。对一些算法/应用程序(FFT、FIR、矩阵乘法和WLAN)获得的仿真结果表明,配置擦洗方案的选择(外部vs内部)对配置内存的大小和重新配置周期的数量都有显著影响(前者分别多20-80%和38倍)。
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Implementation and evaluation of configuration scrubbing on CGRAs: A case study
This paper investigates the overhead imposed by various configuration scrubbing techniques used in fault-tolerant Coarse Grained Reconfigurable Arrays (CGRAs). Today, reconfigurable architectures host large configuration memories. As we progress further in the nanometer regime, these configuration memories have become increasingly susceptible to single event upsets caused e.g. by cosmic radiation. Configuration scrubbing is a frequently used technique to protect these configuration memories against single event upsets. Existing works on configuration scrubbing deal only with FPGA without any reference to the CGRAs (in which configuration memories consume up to 50% of silicon area). Moreover, in the known literature lacks a comprehensive comparison of various configuration scrubbing techniques to guide system designers about the merits/demerits of different scrubbing methods which could be applied to CGRAs. To address these problems, in this paper we classify various configuration scrubbing techniques and quantify their trade-offs when implemented on a CGRA. Synthesis results reveal that scrubbing logic incurs negligible silicon overhead (up to 3% of the area of computational units). Simulation results obtained for a few algorithms/applications (FFT, FIR, matrix multiplication, and WLAN) show that the choice of the configuration scrubbing scheme (external vs. internal) has significant impact on both the size of configuration memory and the number of reconfiguration cycles (respectively 20-80% more and up to 38 times more for the former).
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