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Fast Linear Interpolation

Published: 15 April 2021 Publication History

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.

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Information

Published In

cover image ACM Journal on Emerging Technologies in Computing Systems
ACM Journal on Emerging Technologies in Computing Systems  Volume 17, Issue 2
Hardware and Algorithms for Efficient Machine Learning
April 2021
360 pages
ISSN:1550-4832
EISSN:1550-4840
DOI:10.1145/3446841
  • Editor:
  • Ramesh Karri
Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Publication History

Published: 15 April 2021
Accepted: 01 September 2020
Received: 01 April 2020
Published in JETC Volume 17, Issue 2

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  1. Compiler
  2. interpolation

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