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Arpra: an arbitrary precision range analysis library

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Version 2 2023-06-12, 09:53
Version 1 2023-06-10, 00:05
journal contribution
posted on 2023-06-12, 09:53 authored by James Turner, Thomas NowotnyThomas Nowotny
Motivated by the challenge of investigating the reproducibility of spiking neural network simulations, we have developed the Arpra library: an open source C library for arbitrary precision range analysis based on the mixed Interval Arithmetic (IA)/Affine Arithmetic (AA) method. Arpra builds on this method by implementing a novel mixed trimmed IA/AA, in which the error terms of AA ranges are minimised using information from IA ranges. Overhead rounding error is minimised by computing intermediate values as extended precision variables using the MPFR library. This optimisation is most useful in cases where the ratio of overhead error to range width is high. Three novel affine term reduction strategies improve memory efficiency by merging affine terms of lesser significance. We also investigate the viability of using mixed trimmed IA/AA and other AA methods for studying reproducibility in unstable spiking neural network simulations.

Funding

Brains on Board: Neuromorphic Control of Flying Robots; G1980; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/P006094/1

HBP SGA3 - Human Brain Project Specific Grant Agreement 3; G2945; EUROPEAN UNION; 945539

Human Brain Project Specific Grant Agreement 2 — HBP SGA2; G2410; EUROPEAN UNION; 785907

History

Publication status

  • Published

File Version

  • Published version

Journal

Frontiers in Neuroinformatics

ISSN

1662-5196

Publisher

Frontiers Media

Volume

15

Page range

1-21

Article number

a632729

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2021-06-14

First Open Access (FOA) Date

2021-06-29

First Compliant Deposit (FCD) Date

2021-06-12

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