A Learning-Based Approach to Approximate Coded Computation
This work addresses the problem of extending coded computation beyond matrix polynomials for applications in digital signal processing, representing an incremental advancement.
The paper tackles the limitation of Lagrange coded computation (LCC) to matrix polynomials by proposing AICC, an AI-aided learning approach using deep neural networks for coded computation of more general functions, with numerical simulations demonstrating its suitability for matrix functions in digital signal processing.
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.