论文标题
一种基于学习的方法来近似编码计算
A Learning-Based Approach to Approximate Coded Computation
论文作者
论文摘要
拉格朗日编码计算（LCC）对于以编码的分布式方式解决矩阵多项式的问题至关重要。然而，它只能解决可表示为矩阵多项式的问题。在本文中，我们提出了AICC，这是一种由LCC启发但也使用深神经网络（DNNS）的AI AID学习方法。它适用于更通用函数的编码计算。数值模拟证明了所提出的方法对经常在数字信号处理中经常使用的不同矩阵函数的编码计算的适用性。
Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate for coded computation of more general functions. Numerical simulations demonstrate the suitability of the proposed approach for the coded computation of different matrix functions that are often utilized in digital signal processing.
