论文标题
AR的低复杂体系结构（1）推理
Low-complexity Architecture for AR(1) Inference
论文作者
论文摘要
在这封信中，我们根据签名的$ \ operatatorName {ar}（1）$ process提出了相关系数的低复杂性估计器。引入的近似适合在低功率硬件体系结构中实现。蒙特卡洛模拟显示，所提出的估计器与文献中的竞争方法相当，最大误差为$ 10^{ - 2} $。但是，引入方法的硬件实现在几个相关指标中呈现出很大的优势，与参考方法相比，动态功率的降低超过95％，最大工作频率增加了一倍。
In this Letter, we propose a low-complexity estimator for the correlation coefficient based on the signed $\operatorname{AR}(1)$ process. The introduced approximation is suitable for implementation in low-power hardware architectures. Monte Carlo simulations reveal that the proposed estimator performs comparably to the competing methods in literature with maximum error in order of $10^{-2}$. However, the hardware implementation of the introduced method presents considerable advantages in several relevant metrics, offering more than 95% reduction in dynamic power and doubling the maximum operating frequency when compared to the reference method.
