基于电压过标技术的低功耗精度可配置进位前置加法器

H. Afzali-Kusha, M. Kamal, M. Pedram
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引用次数: 5

摘要

提出了一种低功耗、可配置精度的基于块的进位预判加法器(AC-CLA)。该结构采用过标度电压和近似块数作为近似旋钮,以提高加法器的能量消耗以及可靠性和寿命。前者可以在设计时和运行时中设置,而后者只能在设计时调用。在这个加法器中,对于给定的精度水平,一些模块通过使用过刻度电压在近似模式下工作。基于块的结构可以独立地对每个块施加过刻度电压。加法器的有效性取决于近似块的数量以及用于这些块的VOS电压水平。对于负责具有较高开关活动的较低有效位的块,使用较低的VOS电压水平是降低加法器功耗同时使误差在可容忍范围内的关键。该结构需要很少的电平移位器,使实现开销低。采用15nm FinFET技术研究了AC-CLA结构的效率。研究结果表明,在近似模式下,可实现高达57%的节能。此外,对于这种加法器,BTI引起的加法器的延迟退化在10年内减少了7%,而在相同的工作模式下,这一比例为50%。最后,在神经网络中评估了AC-CLA加法器的分类效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-power Accuracy-configurable Carry Look-ahead Adder Based on Voltage Overscaling Technique
In this paper, a low-power accuracy-configurable block-based Carry Look-ahead Adder (AC-CLA) is proposed. The structure employs the voltage over scaling and number of approximate blocks as the approximation knobs for improving the energy consumption as well as the reliability and lifetime of the adder. 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. In this adder, for a given accuracy level, some of the blocks work in the approximate mode by using over-scaled voltages. The block-based structure enables applying the overscaled voltage for each block independently. The efficacy of the adder depends on the number of the approximate blocks as well as the VOS voltage levels used for these blocks. The use of lower VOS voltage levels for the blocks responsible for lower significant bits which have higher switching activities is the key for reducing the power consumption of the adder while having the error within a tolerable limit. The structure requires few level shifters making the realization overhead low. The efficiency of the AC-CLA structure is studied using a 15 nm FinFET technology. The results of the study indicate that in the approximate mode up to 57% energy saving may be achieved. In addition, for this adder, the BTI induced delay degradation of the adder over 10 years decreases by up to 7% compared to 50% in the case of the exact operating mode. Finally, the efficacy of AC-CLA adder is assessed in a neural network for the classification application.
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