A 0.35V 0.367TOPS/W Image Sensor with 3-Layer Optical-Electronic Hybrid Convolutional Neural Network

📋 论文概要

本文提出了一种0.35V供电、能效为0.367TOPS/W的图像传感器，集成了三层光学-电子混合卷积神经网络。该设计通过像素级和阵列级特征提取，解决了传统图像传感器与云处理或片上AI处理器之间的功耗和数据传输延迟问题。

💡 主要创新点

• 提出光学-电子混合卷积神经网络架构，在传感器层面实现三层卷积计算，减少数据搬运功耗。
• 在0.35V超低电压下工作，实现0.367TOPS/W的高能效，同时保持图像处理能力。
• 通过像素级模拟计算与光学滤波结合，避免传统电容阵列或复杂逻辑导致的填充因子损失。

🏷 关键词

📄 原文摘要

relies on image sensors coupled with cloud processing or on-chip Artificial Intelligence (AI) processors have encountered significant challenges in terms of power consumption, delays arising from data transmission, and/or memory access. In-sensor and near-sensor computing have been reported to solve this issue by applying pixel or array level feature extraction [2-6]. In [2,4], capacitors are utilized for analog-domain Haar filtering to reduce the processing power consumption, but sacrificing the Fill Factor (FF) due to the use of the capacitor array [2], or complicated pixel-level logic [4]. An image sensor in introduced in [3] with Hog feature-based object detection, achieving both low power and high accuracy for the detection of up to three object classes. However, it does not work on more complex tasks. Convolutional Neural

👥 作者与机构

Xuecheng Wang*, Zheng Huang*, Tianyi Liu, Wanxin Shi, Hongwei Chen, Milin Zhang

Tsinghua University, Beijing, China *Equally Credited Authors (ECAs) Traditional computer-vision technology that