This article investigates an energy-efficient accuracy-configurable Dadda (X-Dadda) multiplier. The structure employs the voltage overscaling and approximate width setting as the approximation knobs for improving the energy consumption as well as the reliability and lifetime of the multiplier. While the former may be set in the design time as well as the runtime, the latter may only be invoked in the design time. For a given accuracy level, the partial product columns and the overscaled voltage for optimizing the energy are determined. Normally, to have the error within a tolerable limit, the voltage overscaled columns are those at lower bit significances which have higher switching activities. The structure makes use of a low number of level shifters for a low-overhead realization. The approximate columns which start from the first column are contiguous. To further improve the efficiency of the multiplier, four-bit truncation of the multiplier output is also suggested. The efficiency of the X-Dadda structure is investigated using a 15-nm FinFET technology. The results indicate that, for example, when the approximate mode with the mean relative error distance (MRED) of 0.11 is considered, up to 43% energy saving is achieved. In addition, for this case, the Bias temperature instability (BTI)-induced delay degradation of the multiplier decreases up to 9.9% compared to 50% in the case of the exact mode. Also, the impact of process variations on the accuracy of the X-Dadda is studied. Finally, the efficacy of the X-Dadda multiplier, when used in neural networks for image classification and image-processing applications, is assessed.

中文翻译：

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基于电压超标的提高寿命的节能精度可配置达达乘法器的设计探索

本文研究了一种高能效、可精确配置的 Dadda (X-Dadda) 乘法器。该结构采用电压超标和近似宽度设置作为近似旋钮，以提高乘法器的能耗以及可靠性和寿命。前者可以在设计时和运行时设置，后者只能在设计时调用。对于给定的精度水平，部分乘积列和用于优化能量的超标电压被确定。通常，为了使误差在可容忍的范围内，电压超标列是那些具有较高开关活动的较低位重要性的列。该结构使用少量的电平转换器来实现低开销。从第一列开始的近似列是连续的。为了进一步提高乘法器的效率，还建议对乘法器输出进行四位截断。使用 15 纳米 FinFET 技术研究了 X-Dadda 结构的效率。结果表明，例如，当考虑平均相对误差距离 (MRED) 为 0.11 的近似模式时，可实现高达 43% 的节能。此外，对于这种情况，偏置温度不稳定性 (BTI) 引起的乘法器延迟退化降低了 9.9%，而在精确模式的情况下则为 50%。此外，还研究了工艺变化对 X-Dadda 精度的影响。最后，X-Dadda 乘法器在用于图像分类和图像处理应用的神经网络中时的功效，