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Advancing Direct Convolution Using Convolution Slicing Optimization and ISA Extensions

https://doi.org/10.1145/3625004
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-72789-5_9openalex-snapshot
SWIRL ++ : Evaluating Performance Models to Guide Code Transformation in Convolutional Neural Networks
inferredcites →https://doi.org/10.1007/s10586-021-03494-yopenalex-snapshot
cuConv: CUDA implementation of convolution for CNN inference
inferredcites →https://doi.org/10.1016/j.sysarc.2022.102806openalex-snapshot
Reformulating the direct convolution for high-performance deep learning inference on ARM processors
inferredcites →https://doi.org/10.1109/asap.2017.7995254openalex-snapshot
Parallel Multi Channel convolution using General Matrix Multiplication
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/mm.2022.3176529openalex-snapshot
Compiling for the IBM Matrix Engine for Enterprise Workloads
inferredcites →https://doi.org/10.1109/sbac-pad49847.2020.00023openalex-snapshot
High Performance and Portable Convolution Operators for Multicore Processors
inferredcites →https://doi.org/10.1109/sbac-pad49847.2020.00024openalex-snapshot
High-Performance Low-Memory Lowering: GEMM-based Algorithms for DNN Convolution
inferredcites →https://doi.org/10.1109/sbac-pad53543.2021.00020openalex-snapshot
Efficient Tensor Slicing for Multicore NPUs using Memory Burst Modeling
inferredcites →https://doi.org/10.1145/1356052.1356053openalex-snapshot
Anatomy of high-performance matrix multiplication
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/2764454openalex-snapshot
BLIS: A Framework for Rapidly Instantiating BLAS Functionality
inferredcites →https://doi.org/10.1145/3332466.3374520openalex-snapshot
Optimizing batched winograd convolution on GPUs
inferredcites →https://doi.org/10.1145/3445814.3446759openalex-snapshot
Analytical characterization and design space exploration for optimization of CNNs
inferredcites →https://doi.org/10.1145/3498361.3538940openalex-snapshot
mGEMM
inferredcites →https://doi.org/10.1145/3570305openalex-snapshot
YaConv: Convolution with Low Cache Footprint
inferredcites →https://doi.org/10.1145/3570641openalex-snapshot
Autotuning Convolutions Is Easier Than You Think
inferredcites →https://doi.org/10.48550/arxiv.1408.5093openalex-snapshot
Caffe: Convolutional Architecture for Fast Feature Embedding
inferredcites →https://doi.org/10.48550/arxiv.1410.0759openalex-snapshot
cuDNN: Efficient Primitives for Deep Learning

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-96-2830-8_14openalex-snapshot
VConv: Autotiling Convolution Algorithm Based on MLIR for Multi-core Vector accelerators
inferred← citeshttps://doi.org/10.1007/s42514-024-00200-3openalex-snapshot
Optimizing depthwise separable convolution on DCU
inferred← citeshttps://doi.org/10.1016/j.future.2026.108766openalex-snapshot
A geometry-agnostic matrix-multiplication instruction Set Architecture extension
inferred← citeshttps://doi.org/10.1016/j.parco.2025.103137openalex-snapshot
Multi-level parallelism optimization for two-dimensional convolution vectorization method on multi-core vector accelerator
inferred← citeshttps://doi.org/10.1109/access.2024.3354720openalex-snapshot
SIMD-Constrained Lookup Table for Accelerating Variable-Weighted Convolution on x86/64 CPUs
inferred← citeshttps://doi.org/10.1109/codit66093.2025.11321822openalex-snapshot
High Speed Implementation of Segmentation by PSPNet on a Latest CPU
inferred← citeshttps://doi.org/10.1109/hpec62836.2024.10938425openalex-snapshot
High Performance Im2win and Direct Convolutions Using Three Tensor Layouts on SIMD Architectures
inferred← citeshttps://doi.org/10.1109/jiot.2025.3613691openalex-snapshot
A Survey of AI Inference Technologies for On-Device Systems
inferred← citeshttps://doi.org/10.1145/3773656.3773664openalex-snapshot
High-performance in-situ ML Inference with dalotia: A Lightweight Tensor Loader API for Science Codes
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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