Approximate computing allows compromising accuracy to attain energy and performance efficient designs. However, the accuracy requirements of many applications change on runtime and it has been often observed that traditional approximate hardware tends to either provide unacceptable results or leads to an unnecessary computational effort. Quality scalable configurations can overcome these limitations. With the same motivation, we propose a low-power quality scalable approximate multiplier (LPQ-SAM) in this paper. This low power multiplier has various accuracy reconfigurable modes, including an accurate one and thus, it can be used for both error-resilient and exact applications. LPQ-SAM is exhaustively tested for different error metrics and it has been observed that in the approximate mode, it provides up to 19% and 55% power reduction compared to the exact Booth and Wallace multipliers, respectively. For illustration purposes, we demonstrated the effectiveness of LPQ-SAM on a real-time application, i.e., image masking.

中文翻译：

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LPQ-SAM：一种低功率质量可扩展近似乘法器

近似计算允许牺牲精度以获得能源和性能高效的设计。然而，许多应用程序的精度要求在运行时会发生变化，并且经常观察到传统的近似硬件往往会提供不可接受的结果或导致不必要的计算工作。高质量的可扩展配置可以克服这些限制。出于同样的动机，我们在本文中提出了一种低功率质量可扩展近似乘法器（LPQ-SAM）。这种低功耗乘法器具有多种精度可重新配置模式，包括一种精确模式，因此，它既可用于抗误差应用，也可用于精确应用。LPQ-SAM 针对不同的错误度量进行了详尽的测试，并且观察到在近似模式下，与精确的 Booth 和 Wallace 乘法器相比，它分别提供高达 19% 和 55% 的功率降低。出于说明目的，我们展示了 LPQ-SAM 在实时应用（即图像掩蔽）上的有效性。