Open this publication in new window or tab >>2017 (English)In: Proceedings 2017 IEEE 24th Symposium on Computer Arithmetic (ARITH), London, UK, 24-26 July 2017 / [ed] Neil Burgess, Javier Bruguera, and Florent de Dinechin, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 62-63Conference paper, Published paper (Refereed)
Abstract [en]
Approximate matrix inversion based on Neumann series has seen a recent increased interest motivated by massive MIMO systems. There, the matrices are in many cases diagonally dominant, and, hence, a reasonable approximation can be obtained within a few iterations of a Neumann series. In this work, we clarify that the complexity of exact methods are about the same as when three terms are used for the Neumann series, so in this case, the complexity is not lower as often claimed. The second common argument for Neumann series approximation, higher parallelism, is indeed correct. However, in most current practical use cases, such a high degree of parallelism is not required to obtain a low latency realization. Hence, we conclude that a careful evaluation, based on accuracy and latency requirements must be performed and that exact matrix inversion is in fact viable in many more cases than the current literature claims.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Proceedings Symposium on Computer Arithmetic, ISSN 1063-6889 ; 2017
Keywords
matrix inversion, complexity, parallel processing, massive MIMO
National Category
Computer Systems Signal Processing Communication Systems
Identifiers
urn:nbn:se:liu:diva-139337 (URN)10.1109/ARITH.2017.11 (DOI)000424786700011 ()9781538619650 (ISBN)9781538619643 (ISBN)9781538619667 (ISBN)
Conference
The 24th Symposium on Computer Arithmetic (ARITH), London, UK, 24-26 July 2017
2017-07-102017-07-102019-05-09Bibliographically approved