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Autotuning Convolutions Is Easier Than You Think

https://doi.org/10.1145/3570641
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-06486-4_7openalex-snapshot
Intel Math Kernel Library
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A Comparison of Compiler Tiling Algorithms
inferredcites →https://doi.org/10.1007/bf01379404openalex-snapshot
Some efficient solutions to the affine scheduling problem. Part II. Multidimensional time
inferredcites →https://doi.org/10.1007/bf01407835openalex-snapshot
Some efficient solutions to the affine scheduling problem. I. One-dimensional time
inferredcites →https://doi.org/10.1109/cgo46174.2019openalex-snapshot
2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot
YOLO9000: Better, Faster, Stronger
inferredcites →https://doi.org/10.1109/iccd46524.2019.00053openalex-snapshot
PlaidML-HE: Acceleration of Deep Learning Kernels to Compute on Encrypted Data
inferredcites →https://doi.org/10.1109/sc.2016.83openalex-snapshot
LIBXSMM: Accelerating Small Matrix Multiplications by Runtime Code Generation
inferredcites →https://doi.org/10.1137/16m108968xopenalex-snapshot
High-Performance Tensor Contraction without Transposition
inferredcites →https://doi.org/10.1142/s0129626412500107openalex-snapshot
POLLY — PERFORMING POLYHEDRAL OPTIMIZATIONS ON A LOW-LEVEL INTERMEDIATE REPRESENTATION
inferredcites →https://doi.org/10.1145/1375581.1375595openalex-snapshot
A practical automatic polyhedral parallelizer and locality optimizer
inferredcites →https://doi.org/10.1145/1377603.1377607openalex-snapshot
High-performance implementation of the level-3 BLAS
inferredcites →https://doi.org/10.1145/223428.207162openalex-snapshot
Tile size selection using cache organization and data layout
inferredcites →https://doi.org/10.1145/2400682.2400713openalex-snapshot
Polyhedral parallel code generation for CUDA
inferredcites →https://doi.org/10.1145/2491956.2462176openalex-snapshot
Halide
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Polyhedral AST Generation Is More Than Scanning Polyhedra
inferredcites →https://doi.org/10.1145/2764454openalex-snapshot
BLIS: A Framework for Rapidly Instantiating BLAS Functionality
inferredcites →https://doi.org/10.1145/3033019.3033023openalex-snapshot
Optimization space pruning without regrets
inferredcites →https://doi.org/10.1145/3211346.3211354openalex-snapshot
Diesel: DSL for linear algebra and neural net computations on GPUs
inferredcites →https://doi.org/10.1145/3295500.3356218openalex-snapshot
Analytical cache modeling and tilesize optimization for tensor contractions
inferredcites →https://doi.org/10.1145/3373376.3378508openalex-snapshot
FlexTensor
inferredcites →https://doi.org/10.1145/3445814.3446759openalex-snapshot
Analytical characterization and design space exploration for optimization of CNNs
inferredcites →https://doi.org/10.1145/3559009.3569682openalex-snapshot
Transfer-Tuning
inferredcites →https://doi.org/10.1287/moor.19.4.769openalex-snapshot
A Polynomial Time Algorithm for Counting Integral Points in Polyhedra When the Dimension is Fixed
inferredcites →https://doi.org/10.48550/arxiv.1805.08166openalex-snapshot
Learning to Optimize Tensor Programs

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Comparative Study of Neural Network Compilers on ARMv8 Architecture
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Extending a predictable machine learning framework with efficient gemm-based convolution routines
inferred← citeshttps://doi.org/10.1016/j.sysarc.2023.102921openalex-snapshot
XFC: Enabling automatic and fast operator synthesis for mobile deep learning compilation
inferred← citeshttps://doi.org/10.1109/rtas61025.2024.00013openalex-snapshot
A Predictable SIMD Library for GEMM Routines
inferred← citeshttps://doi.org/10.1109/tc.2025.3551937openalex-snapshot
Deep Learning Operators Performance Tuning for Changeable Sized Input Data on Tensor Accelerate Hardware
inferred← citeshttps://doi.org/10.1109/tpds.2023.3322037openalex-snapshot
Design and Implementation of Deep Learning 2D Convolutions on Modern CPUs
inferred← citeshttps://doi.org/10.1145/3609024.3609410openalex-snapshot
Rank-Polymorphism for Shape-Guided Blocking
inferred← citeshttps://doi.org/10.1145/3625004openalex-snapshot
Advancing Direct Convolution Using Convolution Slicing Optimization and ISA Extensions
inferred← citeshttps://doi.org/10.1145/3649153.3649194openalex-snapshot
Register Blocking: An Analytical Modelling Approach for Affine Loop Kernels
inferred← citeshttps://doi.org/10.1145/3650109openalex-snapshot
The Droplet Search Algorithm for Kernel Scheduling
inferred← citeshttps://doi.org/10.1145/3688609openalex-snapshot
DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration
inferred← citeshttps://doi.org/10.1145/3696443.3708952openalex-snapshot
A Multi-level Compiler Backend for Accelerated Micro-kernels Targeting RISC-V ISA Extensions
inferred← citeshttps://doi.org/10.1145/3747183openalex-snapshot
Register Blocking: A Source-to-Source Analytical Modelling Approach for Affine Loop Kernels
inferred← citeshttps://doi.org/10.1145/3806056openalex-snapshot
A Decoupled Analytical Model for Tile Size Selection in Affine Programs
inferred← citeshttps://doi.org/10.1145/3815112openalex-snapshot
Analytical Modeling of Set-Associative Caches for Optimizing Tensor Operations
inferred← citeshttps://doi.org/10.3390/electronics13183765openalex-snapshot
Efficient Convolutional Neural Networks Utilizing Fine-Grained Fast Fourier Transforms
inferred← citeshttps://doi.org/10.47749/t/unicamp.2023.1374643openalex-snapshot
Convolution tensor slicing optimization for multicore NPUs
inferred← citeshttps://doi.org/10.47749/t/unicamp.2023.1376865openalex-snapshot
Improving direct convolution through tensor slicing, vectorized packing and ISA extensions
inferred← citeshttps://doi.org/10.5753/ctd.2024.2901openalex-snapshot
Improving Direct Convolution through Tensor Slicing, Vectorized Packing and ISA Extensions
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