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首页> 外文会议>2019 56th ACM/IEEE Design Automation Conference >autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
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autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

机译：autoAx：利用近似组件库的自动设计空间探索和电路构建方法

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Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary approximate circuits have already been proposed to simplify the design process of approximate accelerators. Because these libraries contain from tens to thousands of approximate implementations for a single arithmetic operation it is intractable to find an optimal combination of approximate circuits in the library even for an application consisting of a few operations. An open problem is “how to effectively combine circuits from these libraries to construct complex approximate accelerators”. This paper proposes a novel methodology for searching, selecting and combining the most suitable approximate circuits from a set of available libraries to generate an approximate accelerator for a given application. To enable fast design space generation and exploration, the methodology utilizes machine learning techniques to create computational models estimating the overall quality of processing and hardware cost without performing full synthesis at the accelerator level. Using the methodology, we construct hundreds of approximate accelerators (for a Sobel edge detector) showing different but relevant tradeoffs between the quality of processing and hardware cost and identify a corresponding Pareto-frontier. Furthermore, when searching for approximate implementations of a generic Gaussian filter consisting of 17 arithmetic operations, the proposed approach allows us to identify approximately 103 highly relevant implementations from 1023 possible solutions in a few hours, while the exhaustive search would take four months on a high-end processor.
机译：近似计算是开发高效节能的计算系统（例如各种加速器）的新兴范例。在文献中，已经提出了许多基本近似电路库来简化近似加速器的设计过程。因为这些库包含单个算术运算的数十到数千个近似实现，所以即使对于包含几个运算的应用，也很难在该库中找到近似电路的最佳组合。一个开放的问题是“如何有效地组合这些库中的电路以构造复杂的近似加速器”。本文提出了一种新颖的方法，用于从一组可用的库中搜索，选择和组合最合适的近似电路，以生成给定应用的近似加速器。为了实现快速的设计空间生成和探索，该方法利用机器学习技术来创建计算模型，该模型可估计处理的整体质量和硬件成本，而无需在加速器级别执行完全综合。使用该方法，我们构造了数百个近似加速器（用于Sobel边缘检测器），显示了处理质量和硬件成本之间的不同但相关的权衡，并确定了相应的帕累托边界。此外，当搜索由17个算术运算组成的通用高斯滤波器的近似实现时，所提出的方法使我们能够识别大约10 \ n 3 \ n来自10 \ n 23 \ n几个小时内可能的解决方案，而在高端处理器上进行详尽的搜索将需要四个月的时间。

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