Reformulating the direct convolution for high-performance deep learning inference on ARM processors-Reference-Cited by-同舟云学术

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

Published:2023-02 Issue: Volume:135 Page:102806
ISSN:1383-7621
Container-title:Journal of Systems Architecture
language:en
Short-container-title:Journal of Systems Architecture

Author:

Barrachina Sergio,Castelló Adrián,Dolz Manuel F.^ORCID,Low Tze Meng,Martínez Héctor,Quintana-Ortí Enrique S.,Sridhar Upasana,Tomás Andrés E.

Publisher

Elsevier BV

Subject

Hardware and Architecture,Software

Reference33 articles.

1. Demystifying parallel and distributed deep learning: An in-depth concurrency analysis;Ben-Nun;ACM Comput. Surv.,2019

2. Efficient processing of deep neural networks: A tutorial and survey;Sze;Proc. IEEE,2017

3. K. Chellapilla, S. Puri, P. Simard, High performance convolutional neural networks for document processing, in: International Workshop on Frontiers in Handwriting Recognition, 2006.

4. Anatomy of high-performance deep learning convolutions on SIMD architectures;Georganas,2018

5. High performance and portable convolution operators for multicore processors;San Juan,2020

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advancing Direct Convolution Using Convolution Slicing Optimization and ISA Extensions;ACM Transactions on Architecture and Code Optimization;2023-12-14

2. Optimizing Direct Convolutions on ARM Multi-Cores;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11

3. SMaLL: Software for rapidly instantiating Machine Learning Libraries;ACM Transactions on Embedded Computing Systems;2023-07-12

4. Analysis and Optimization of Direct Convolution Execution on Multi-Core Processors;IEEE Access;2023

5. Parallel Gemm-Based Convolutions for Deep Learning on Multicore Arm and Risc-V Architectures;2023