Deep Learning Performance Characterization on GPUs for Various Quantization Frameworks

IF 3.1 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE AI (Basel, Switzerland) Pub Date : 2023-10-18 DOI:10.3390/ai4040047
Muhammad Ali Shafique, Arslan Munir, Joonho Kong
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Abstract

Deep learning is employed in many applications, such as computer vision, natural language processing, robotics, and recommender systems. Large and complex neural networks lead to high accuracy; however, they adversely affect many aspects of deep learning performance, such as training time, latency, throughput, energy consumption, and memory usage in the training and inference stages. To solve these challenges, various optimization techniques and frameworks have been developed for the efficient performance of deep learning models in the training and inference stages. Although optimization techniques such as quantization have been studied thoroughly in the past, less work has been done to study the performance of frameworks that provide quantization techniques. In this paper, we have used different performance metrics to study the performance of various quantization frameworks, including TensorFlow automatic mixed precision and TensorRT. These performance metrics include training time and memory utilization in the training stage along with latency and throughput for graphics processing units (GPUs) in the inference stage. We have applied the automatic mixed precision (AMP) technique during the training stage using the TensorFlow framework, while for inference we have utilized the TensorRT framework for the post-training quantization technique using the TensorFlow TensorRT (TF-TRT) application programming interface (API).We performed model profiling for different deep learning models, datasets, image sizes, and batch sizes for both the training and inference stages, the results of which can help developers and researchers to devise and deploy efficient deep learning models for GPUs.
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不同量化框架下gpu的深度学习性能表征
深度学习在许多应用中都有应用,比如计算机视觉、自然语言处理、机器人和推荐系统。庞大而复杂的神经网络带来了高准确率;然而,它们会对深度学习性能的许多方面产生不利影响,例如训练时间、延迟、吞吐量、能量消耗以及训练和推理阶段的内存使用。为了解决这些挑战,已经开发了各种优化技术和框架,以便在训练和推理阶段有效地执行深度学习模型。尽管量化等优化技术在过去已经得到了深入的研究,但研究提供量化技术的框架的性能的工作却很少。在本文中,我们使用不同的性能指标来研究各种量化框架的性能,包括TensorFlow自动混合精度和TensorRT。这些性能指标包括训练阶段的训练时间和内存利用率,以及推理阶段图形处理单元(gpu)的延迟和吞吐量。我们在训练阶段使用TensorFlow框架应用了自动混合精度(AMP)技术,而在推理方面,我们使用TensorFlow TensorRT (TF-TRT)应用程序编程接口(API)将TensorRT框架用于训练后量化技术。我们在训练和推理阶段对不同的深度学习模型、数据集、图像大小和批处理大小进行了模型分析,其结果可以帮助开发人员和研究人员为gpu设计和部署高效的深度学习模型。
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来源期刊
CiteScore
7.20
自引率
0.00%
发文量
0
审稿时长
11 weeks
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