Effect of Process Variations in Digital Pixel Circuits on the Accuracy of DNN based Smart Sensor

Minah Lee, Mandovi Mukherjee, Priyabrata Saha, Mohammad Faisal Amir, Taesik Na, Saibal Mukhopadhyay

Abstract: The digital pixel based image sensors with 3D integrated and pixel-parallel read-out-integrated-circuits (ROIC) show potential for high resolution and high frame rate in many mission critical surveillance applications. However, fixed pattern noise (FPN) caused by process variations of ROIC aggravates the quality of image and further degrades the performance of deep neural network (DNN). This paper studies the effect of process variations in digital pixel circuits and resulting image noise on the accuracy of a DNN. We propose a digital pixel-DNN cross-layer simulation methodology for accurate training and evaluation of DNN under noise induced from process variations. The simulation results show that the process variation in the digital pixel creates distinct noise structure and should be accurately considered while training a DNN. We also present design space explorations using our cross-layer simulation.