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A 0.8 V Intelligent Vision Sensor With Tiny Convolutional Neural Network and Programmable Weights Using Mixed-Mode Processing-in-Sensor Technique for Image Classification
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A 0.8 V Intelligent Vision Sensor With Tiny Convolutional Neural Network and Programmable Weights Using Mixed-Mode Processing-in-Sensor Technique for Image Classification

Tzu-Hsiang Hsu, Guan-Cheng Chen, Yi-Ren Chen, Ren-Shuo Liu, Chung-Chuan Lo, Kea-Tiong Tang, Meng-Fan Chang 和 Chih-Cheng Hsieh
IEEE Journal of Solid-State Circuits, 卷.58(11), 頁碼.3266-3274
01/11/2023
Web of Science ID: WOS:001025528900001

摘要

Artificial intelligent (AI) Convolution convolutional neural network (CNN) CMOS image sensor (CIS) Convolutional neural networks face detection (FD) feature extraction Frequency modulation intelligent vision sensor (IVS) Kernel processing-in-sensor (PIS) Pulse width modulation Task analysis Vision sensors
This article presents an intelligent vision sensor (IVS) with embedded tiny convolutional neural network (CNN) model and programmable processing-in-sensor (PIS) circuit for real-time inference applications of low-power edge devices. The proposed imager realizes the full computing functions of a customized three-layers tiny network, which includes a 3 × 3 convolution layer (stride = 3) with activation function of rectified linear unit (ReLU), a 2 × 2 maximum pooling (MP) layer (stride = 2), and a 1 × 1 fully connected (FC) layer for inference. A 0.8 V 128 × 128 IVS prototype was fabricated and verified in TSMC 0.18 μ m standard CMOS technology. In normal image mode, it consumed 76.4 μ W with full-resolution ( 126 × 126 active resolution) image output at 125 f/s. In CNN mode, it consumed 134.5 μ W at 250 f/s and an achieved iFoMs of 33.8 pJ/pixel ⋅ frame. Using the proposed mixed-mode PIS circuits, the prototype is configured to demonstrate a "human face or not detection" task with an achieved accuracy of 93.6%.

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