A Learning-Based Approach to Approximate Coded Computation

• URL: http://arxiv.org/abs/2205.09818v1
• Date: Thu, 19 May 2022 19:43:53 GMT
• Title: A Learning-Based Approach to Approximate Coded Computation
• Authors: Navneet Agrawal, Yuqin Qiu, Matthias Frey, Igor Bjelakovic, Setareh Maghsudi, Slawomir Stanczak, Jingge Zhu
• Abstract summary: 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.
• Score: 22.886991943938703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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