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Reformulating the direct convolution for high-performance deep learning inference on ARM processors

https://doi.org/10.1016/j.sysarc.2022.102806
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What this work says

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

inferredcites →https://doi.org/10.1002/9780470495094openalex-snapshot
Parallel Solution of Integral Equation‐Based EM Problems in the Frequency Domain
inferredcites →https://doi.org/10.1007/978-1-4615-4873-7openalex-snapshot
High-Performance Computing
inferredcites →https://doi.org/10.1007/978-3-030-29400-7openalex-snapshot
Euro-Par 2019: Parallel Processing
inferredcites →https://doi.org/10.1007/s11227-021-03673-zopenalex-snapshot
PyDTNN: A user-friendly and extensible framework for distributed deep learning
inferredcites →https://doi.org/10.1016/j.sysarc.2022.102459openalex-snapshot
High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS
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/cvpr12618.2008openalex-snapshot
2008 IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/icpads.2012.97openalex-snapshot
Model-driven Level 3 BLAS Performance Optimization on Loongson 3A Processor
inferredcites →https://doi.org/10.1109/ipdps35014.2016openalex-snapshot
2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
inferredcites →https://doi.org/10.1109/jproc.2017.2761740openalex-snapshot
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
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/sc32763.2018openalex-snapshot
SC18: International Conference for High Performance Computing, Networking, Storage and Analysis
inferredcites →https://doi.org/10.1145/1356052.1356053openalex-snapshot
Anatomy of high-performance matrix multiplication
inferredcites →https://doi.org/10.1145/2755561openalex-snapshot
The BLIS Framework
inferredcites →https://doi.org/10.1145/2764454openalex-snapshot
BLIS: A Framework for Rapidly Instantiating BLAS Functionality
inferredcites →https://doi.org/10.1145/2925987openalex-snapshot
Analytical Modeling Is Enough for High-Performance BLIS
inferredcites →https://doi.org/10.1145/77626.79170openalex-snapshot
A set of level 3 basic linear algebra subprograms
inferredcites →https://doi.org/10.23919/date.2018.8342188openalex-snapshot
Block convolution: Towards memory-efficient inference of large-scale CNNs on FPGA
inferredcites →https://doi.org/10.48550/arxiv.1802.09941openalex-snapshot
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
inferredcites →https://doi.org/10.48550/arxiv.1809.07851openalex-snapshot
FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4676008openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-031-30445-3_20openalex-snapshot
Performance Analysis of Convolution Algorithms for Deep Learning on Edge Processors
inferred← citeshttps://doi.org/10.1007/978-3-031-40843-4_44openalex-snapshot
GEMM-Like Convolution for Deep Learning Inference on the Xilinx Versal
inferred← citeshttps://doi.org/10.1007/s11227-025-07706-9openalex-snapshot
Batched data layout optimization for Im2col-based convolutions on CPUs
inferred← citeshttps://doi.org/10.1016/j.sysarc.2024.103186openalex-snapshot
Parallel GEMM-based convolutions for deep learning on multicore ARM and RISC-V architectures
inferred← citeshttps://doi.org/10.1016/j.sysarc.2024.103236openalex-snapshot
Optimization of block-scaled integer GeMMs for efficient DNN deployment on scalable in-order vector processors
inferred← citeshttps://doi.org/10.1109/access.2023.3283312openalex-snapshot
Analysis and Optimization of Direct Convolution Execution on Multi-Core Processors
inferred← citeshttps://doi.org/10.1109/jiot.2024.3395335openalex-snapshot
Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors
inferred← citeshttps://doi.org/10.1109/jiot.2025.3613691openalex-snapshot
A Survey of AI Inference Technologies for On-Device Systems
inferred← citeshttps://doi.org/10.1109/pdp59025.2023.00043openalex-snapshot
Toward Matrix Multiplication for Deep Learning Inference on the Xilinx Versal
inferred← citeshttps://doi.org/10.1109/rtas61025.2024.00013openalex-snapshot
A Predictable SIMD Library for GEMM Routines
inferred← citeshttps://doi.org/10.1109/sbac-pad55451.2022.00027openalex-snapshot
Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor
inferred← citeshttps://doi.org/10.1145/3581784.3607107openalex-snapshot
Optimizing Direct Convolutions on ARM Multi-Cores
inferred← citeshttps://doi.org/10.1145/3607870openalex-snapshot
SMaLL: Software for Rapidly Instantiating Machine Learning Libraries
inferred← citeshttps://doi.org/10.1145/3625004openalex-snapshot
Advancing Direct Convolution Using Convolution Slicing Optimization and ISA Extensions
inferred← citeshttps://doi.org/10.1145/3650200.3656620openalex-snapshot
Optimizing Attention by Exploiting Data Reuse on ARM Multi-core CPUs
inferred← citeshttps://doi.org/10.1145/3759916openalex-snapshot
Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference
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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