Approximate computing (AC) is an emerging computing paradigm suitable for intrinsic error-tolerant applications to reduce energy consumption and execution time. Different approximate techniques and designs, at both hardware and software levels, have been proposed and demonstrated the effectiveness of relaxing the average output quality constraint. However, the output quality of AC is highly input-dependent, i.e., for some input data, the output errors may reach unacceptable levels. Therefore, there is a dire need for an input-dependent tunable approximate design. With this motivation, in this article, we propose a lightweight and efficient machine-learning-based approach to build an input-aware design selector, i.e., quality controller , to adapt the approximate design in order to meet the target output quality (TOQ). For illustration purposes, we use a library of 8-bit and 16-bit energy-efficient approximate array multipliers with 20 different settings, which are commonly used in image and audio processing applications. The simulation results, based on two sets of images, including an 8 Scene Categories Dataset, which is a benchmark of images data set, demonstrate the effectiveness of the lightweight selector where the proposed tunable design achieves a significant reduction in quality loss with relatively low overhead.

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基于机器学习的近似计算自校正设计

近似计算（AC）是一种新兴的计算范例，适用于内在的容错应用程序，以减少能耗和执行时间。在硬件和软件级别上，已经提出了不同的近似技术和设计，并证明了放宽硬件设计的有效性。平均输出质量约束。但是，AC的输出质量在很大程度上取决于输入，即，对于某些输入数据，输出误差可能达到不可接受的水平。因此，迫切需要与输入有关的可调近似设计。出于这种动机，在本文中，我们提出了一种轻量级且高效的基于机器学习的方法来构建可识别输入的设计选择器，即质量控制员 ，以适应近似设计以满足目标输出质量（TOQ）。出于说明目的，我们使用具有20种不同设置的8位和16位高能效近似阵列乘法器库，这些库通常在图像和音频处理应用程序中使用。仿真结果基于两组图像，其中包括8个场景类别数据集， 它是图像数据集的基准，证明了轻巧选择器的有效性，其中所提出的可调设计以相对较低的开销实现了质量损失的显着降低。