A Learning-Based Approach to Approximate Coded Computation

被引：2

作者：

Agrawal, Navneet ^{[1
]}

Qiu, Yuqin ^{[2
]}

Frey, Matthias ^{[1
]}

Bjelakovic, Igor ^{[3
]}

Maghsudi, Setareh ^{[3
,4
]}

Stanczak, Slawomir ^{[1
,3
]}

Zhu, Jingge ^{[2
]}

机构：

[1] Tech Univ Berlin, Berlin, Germany

[2] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic, Australia

[3] Fraunhofer Heinrich Hertz Inst, Berlin, Germany

[4] Univ Tubingen, Dept Comp Sci, Tubingen, Germany

来源：

2022 IEEE INFORMATION THEORY WORKSHOP (ITW) | 2022年

基金：

澳大利亚研究理事会;

关键词：

D O I：

10.1109/ITW54588.2022.9965865

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：600 / 605

页数：6

共 23 条

[1]

Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265

[2] A NEW SCALING AND SQUARING ALGORITHM FOR THE MATRIX EXPONENTIAL [J].

Al-Mohy, Awad H. ;

Higham, Nicholas J. .

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2009, 31 (03) :970-989

[3] Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems [J].

Alkhateeb, Ahmed ;

Heath, Robert W., Jr. .

IEEE TRANSACTIONS ON COMMUNICATIONS, 2016, 64 (05) :1801-1818

[4] Computation Scheduling for Distributed Machine Learning With Straggling Workers [J].

Amiri, Mohammad Mohammadi ;

Gunduz, Deniz .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2019, 67 (24) :6270-6284

[5] Note on best possible bounds for determinants of matrices close to the identity matrix [J].

Brent, Richard P. ;

Osborn, Judy-anne H. ;

Smith, Warren D. .

LINEAR ALGEBRA AND ITS APPLICATIONS, 2015, 466 :21-26

[6]

Chang WT, 2019, INT CONF ACOUST SPEE, P8187, DOI 10.1109/ICASSP.2019.8682895

[7] APPROXIMATE WEIGHTED CR CODED MATRIX MULTIPLICATION [J].

Charalambides, Neophytos ;

Pilanci, Mert ;

Hero, Alfred O., III .

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, :5095-5099

[8]

Chen L., 2018, INT C MACHINE LEARNI, V80, P903

[9]

Cong Li, 2016, 2016 9th Workshop on Many-Task Computing on Clouds, Grids and Supercomputers (MTAGS). Proceedings, P1, DOI 10.1109/MTAGS.2016.04

[10] On the Optimal Recovery Threshold of Coded Matrix Multiplication [J].

Dutta, Sanghamitra ;

Fahim, Mohammad ;

Haddadpour, Farzin ;

Jeong, Haewon ;

Cadambe, Viveck ;

Grover, Pulkit .

IEEE TRANSACTIONS ON INFORMATION THEORY, 2020, 66 (01) :278-301

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