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Fast Linear Interpolation

Fast Linear Interpolation We present fast implementations of linear interpolation operators for piecewise linear functions and multi-dimensional look-up tables. These operators are common for efficient transformations in image processing and are the core operations needed for lattice models like deep lattice networks, a popular machine learning function class for interpretable, shape-constrained machine learning. We present new strategies for an efficient compiler-based solution using MLIR to accelerate linear interpolation. For real-world machine-learned multi-layer lattice models that use multidimensional linear interpolation, we show these strategies run 5-10 faster on a standard CPU compared to an optimized C++ interpreter implementation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Journal on Emerging Technologies in Computing Systems (JETC) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2021 Owner/Author
ISSN
1550-4832
eISSN
1550-4840
DOI
10.1145/3423184
Publisher site
See Article on Publisher Site

Abstract

We present fast implementations of linear interpolation operators for piecewise linear functions and multi-dimensional look-up tables. These operators are common for efficient transformations in image processing and are the core operations needed for lattice models like deep lattice networks, a popular machine learning function class for interpretable, shape-constrained machine learning. We present new strategies for an efficient compiler-based solution using MLIR to accelerate linear interpolation. For real-world machine-learned multi-layer lattice models that use multidimensional linear interpolation, we show these strategies run 5-10 faster on a standard CPU compared to an optimized C++ interpreter implementation.

Journal

ACM Journal on Emerging Technologies in Computing Systems (JETC)Association for Computing Machinery

Published: Apr 15, 2021

Keywords: Compiler

References