众包自定义可重构建筑设计空间探索的映射问题

Anil Kumar Sistla, Krunalkumar Patel, Gayatri Mehta
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引用次数: 1

摘要

在便携式/可穿戴电子产品的设计中,最大的挑战之一是在一个微小的低功耗封装中实现最佳的效率和灵活性。粗粒度可重构架构(CGRAs)为应用程序领域的低功耗、高性能和灵活设计带来了巨大的希望。由于能够根据应用程序域高度定制这种体系结构,CGRAs非常有前途。然而,更大的定制使得将应用程序映射到这些体系结构非常具有挑战性。需要良好的工具和快速有效的映射算法来支持CGRAs的设计空间探索。特别是,映射问题一直难以以一种令人满意和通用的方式解决。在本文中,我们提出了一个架构设计流程,使用众包来提供基准到新架构的映射。我们表明,在几乎所有情况下,人群可以提供高质量,可靠的映射,显著优于我们自定义的模拟退火算法。我们进一步表明,人群可以提供难以从自动映射算法中获得的其他类型的反馈。我们的概念证明跨架构研究支持8Way或4Way1Hop架构作为首选,结论是限制输入和输出的自定义修改消耗更少的能量,但需要更多的面积,并建议考虑条带架构,因为它们的性能几乎和我们的网格变量一样好,并且可能为人群或自动映射算法提供更直接的映射问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crowdsourcing the mapping problem for design space exploration of custom reconfigurable architecture designs
One of the grand challenges in the design of portable/wearable electronics is to achieve optimal efficiency and flexibility in a tiny low power package. Coarse grained reconfigurable architectures (CGRAs) hold great promise for low power, high performance, and flexible designs for a domain of applications. CGRAs are very promising due to the ability to highly customize such architectures to an application domain. However, greater customization makes the mapping of applications onto these architectures very challenging. Good tools and fast, effective mapping algorithms are needed to support design space exploration for CGRAs. In particular, the mapping problem has been difficult to solve in a satisfying and general way. In this paper, we present an architectural design flow using crowdsourcing to provide mappings of benchmarks onto new architectures. We show that the crowd can provide high quality, reliable mappings, significantly outperforming our custom Simulated Annealing algorithm in almost all cases. We further show that the crowd can provide other types of feedback that are difficult to obtain from an automatic mapping algorithm. Our proof of concept cross-architectural study supports an 8Way or 4Way1Hop architecture as a top choice, concludes that a custom modification that constrains inputs and outputs consumes less energy but requires more area than its less constrained counterpart, and suggests that Stripe architectures are interesting to consider because they perform nearly as well as our mesh variants and may present a more straightforward mapping problem for the crowd or an automatic mapping algorithm.
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