使用高效的基于gemm的卷积例程扩展可预测的机器学习框架

IF 1.4 4区计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS

Real-Time Systems Pub Date : 2023-08-28 DOI:10.1007/s11241-023-09407-z

Iryna De Albuquerque Silva, Thomas Carle, A. Gauffriau, C. Pagetti

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Extending a predictable machine learning framework with efficient gemm-based convolution routines

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Extending a predictable machine learning framework with efficient gemm-based convolution routines

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来源期刊

Real-Time Systems 工程技术-计算机：理论方法

CiteScore

2.90

自引率

7.70%

发文量

审稿时长

6 months

期刊介绍： Papers published in Real-Time Systems cover, among others, the following topics: requirements engineering, specification and verification techniques, design methods and tools, programming languages, operating systems, scheduling algorithms, architecture, hardware and interfacing, dependability and safety, distributed and other novel architectures, wired and wireless communications, wireless sensor systems, distributed databases, artificial intelligence techniques, expert systems, and application case studies. Applications are found in command and control systems, process control, automated manufacturing, flight control, avionics, space avionics and defense systems, shipborne systems, vision and robotics, pervasive and ubiquitous computing, and in an abundance of embedded systems.