Résumé: 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 machinelearned 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.

Langue: Anglais