Fast Linear Interpolation

ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS

Article

Zhang, Nathan^1,4  Canini, Kevin^2,3  Silva, Sean³  Gupta, Maya^2,3 

[1] Stanford Univ, Stanford, CA 94305 USA.;Google Res, Mountain View, CA USA.;Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA USA.;Gates Comp Sci Bldg,353 Jane Stanford Way, Stanford, CA 94305 USA.

关键词: Compiler;    interpolation;   

DOI  :  10.1145/3423184

来源: SCIE

【 摘 要 】

We present fast implementations of linear interpolation operators for piecewise linear functions and multidimensional 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 - 10x faster on a standard CPU compared to an optimized C++ interpreter implementation.

【 授权许可】

Free