Image Signal Processing in the Context of Deep Learning Applications

Ali Кhusein, Urquhart
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Abstract

Deep learning accelerators are a specialized sort of hardware architecture designed to enhance the computational efficiency of computers engaged in deep neural networks (DNNs) training. The implementation of DNNs in embedded vision applications might potentially be facilitated by the integration of energy-effective accelerators of deep learning into sensors. The lack of recognition for their significant impact on accuracy is a notable oversight. In previous iterations of deep learning accelerators integrated inside sensors, a common approach was bypassing the image signal processor (ISP). This deviation from the traditional vision pipelines had a detrimental impact on the performance of machine learning models trained on data that had undergone post-ISP processing. In this study, we establish a set of energy-efficient techniques that allow ISP to maximize their advantages while also limiting the covariate shift between the target dataset (RAW images) and the training dataset (ISP-analyzed images). This approach enables the practical use of in-sensor accelerators. To clarify, our results do not minimize the relevance of in-sensor accelerators. Instead, we highlight deficiencies in the methodology used in prior research and propose methodologies that empower in-sensor accelerators to fully exploit their capabilities.
深度学习应用背景下的图像信号处理
深度学习加速器是一种专门的硬件架构,旨在提高从事深度神经网络(DNN)训练的计算机的计算效率。将高能效的深度学习加速器集成到传感器中,有可能促进深度神经网络在嵌入式视觉应用中的实施。但是,人们却没有认识到它们对准确性的重要影响,这是一个明显的疏忽。在以前集成到传感器中的深度学习加速器的迭代中,一种常见的方法是绕过图像信号处理器(ISP)。这种偏离传统视觉流水线的做法对经过 ISP 后处理的数据所训练的机器学习模型的性能产生了不利影响。在本研究中,我们建立了一套高能效技术,既能让 ISP 最大限度地发挥其优势,又能限制目标数据集(RAW 图像)和训练数据集(经过 ISP 分析的图像)之间的协变量偏移。这种方法使传感器内加速器得以实际应用。需要说明的是,我们的结果并没有贬低传感器内加速器的相关性。相反,我们强调了先前研究中使用的方法存在的不足,并提出了能使传感器内加速器充分发挥其能力的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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