非迭代训练递归神经网络的优化并行实现

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Julia El Zini, Yara Rizk, M. Awad
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引用次数: 11

摘要

递归神经网络(RNN)已经成功地应用于各种顺序决策任务、自然语言处理应用和时间序列预测。这种网络通常通过时间反向传播(BPTT)进行训练,这是非常昂贵的,特别是当时间依赖关系的长度和隐藏神经元的数量增加时。为了减少训练时间,极限学习机(elm)最近被应用于RNN训练,在一些应用中达到了99%的加速。由于ELM训练的非迭代性质,当并行化时,它有可能达到比BPTT更高的速度。在这项工作中,我们提出了Opt-PR-ELM,一种基于ELM的优化并行RNN训练算法,它利用GPU共享内存和并行QR分解算法来有效地获得最优解。在LSTM和GRU等6种RNN体系结构上对该算法进行了理论分析,并在10个时间序列预测应用中对其性能进行了实证检验。与并行BPTT相比,Opt-PR-ELM的速度提高了461倍,所需的训练时间减少了20倍。在实时应用和物联网环境中,新一代cpu的高速度至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimized Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks
Abstract Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and the number of hidden neurons increase. To reduce the training time, extreme learning machines (ELMs) have been recently applied to RNN training, reaching a 99% speedup on some applications. Due to its non-iterative nature, ELM training, when parallelized, has the potential to reach higher speedups than BPTT. In this work, we present Opt-PR-ELM, an optimized parallel RNN training algorithm based on ELM that takes advantage of the GPU shared memory and of parallel QR factorization algorithms to efficiently reach optimal solutions. The theoretical analysis of the proposed algorithm is presented on six RNN architectures, including LSTM and GRU, and its performance is empirically tested on ten time-series prediction applications. Opt-PR-ELM is shown to reach up to 461 times speedup over its sequential counterpart and to require up to 20x less time to train than parallel BPTT. Such high speedups over new generation CPUs are extremely crucial in real-time applications and IoT environments.
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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