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Cross-Layer Noise Analysis in Smart Digital Pixel Sensors with Integrated Deep Neural Network
IEEE Journal on Emerging and Selected Topics in Circuits and Systems ( IF 4.6 ) Pub Date : 2020-12-01 , DOI: 10.1109/jetcas.2020.3031869
Minah Lee , Mandovi Mukherjee , Edward Lee , Priyabrata Saha , Mohammad Faisal Amir , Taesik Na , Saibal Mukhopadhyay

Digital pixel based image sensors with embedded deep neural network (DNN) allow many mission critical surveillance applications. However, image noise caused by variations and non-idealities in the sensor aggravates the quality of image and further degrades the performance of a DNN. We propose a digital pixel-DNN cross-layer simulation methodology for accurate training and evaluation of a DNN under image noise induced from sensors. In particular, this paper focuses on the image noise derived from device mismatches in digital pixel circuits with 3D integrated and pixel-parallel readout circuits, and studies the effect of the resulting image noise on the accuracy of a DNN. The simulation results show that the device mismatch in the digital pixel creates distinct noise structure on output image and should be accurately considered while training a DNN. We also present design space explorations using our cross-layer simulation.

中文翻译:

具有集成深度神经网络的智能数字像素传感器中的跨层噪声分析

具有嵌入式深度神经网络 (DNN) 的基于数字像素的图像传感器支持许多关键任务监视应用。然而,由传感器中的变化和非理想性引起的图像噪声会加剧图像质量并进一步降低 DNN 的性能。我们提出了一种数字像素-DNN 跨层模拟方法,用于在传感器引起的图像噪声下准确训练和评估 DNN。特别是,本文重点关注具有 3D 集成和像素并行读出电路的数字像素电路中设备失配导致的图像噪声,并研究由此产生的图像噪声对 DNN 精度的影响。仿真结果表明,数字像素中的设备失配会在输出图像上产生明显的噪声结构,在训练 DNN 时应准确考虑。
更新日期:2020-12-01
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