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

📋 论文概要

该论文提出了一种0.8V低电压智能视觉传感器，集成了小型卷积神经网络和可编程权重，采用混合模式处理，旨在解决传统成像器加AI加速器方案在低功耗边缘设备中实时推理时的功耗和延迟问题。

💡 主要创新点

• 提出在传感器内集成小型卷积神经网络和可编程权重，实现近传感器处理，减少原始图像数据传输带来的功耗和延迟。
• 采用混合模式（Mixed-Mode）处理架构，在低电压（0.8V）下实现高效计算，适用于低功耗边缘AI应用。

🏷 关键词

📄 原文摘要

artificial intelligence (AI) for applications requiring image classification are in growing demand. However, the imager plus dedicated AI accelerator solution [1] suffers from the burdens of power and latency caused by the raw image data traffic between the imager and the companion signal processor with a neural network accelerator, making it unsuitable for the real-time inference in low-power edge devices. Recently, imagers with near- or in-sensor processing capability have been developed [2-6] to improve the system efficiency for specific applications. In [2-4], the near-sensor Haar-like filtering operations are implemented in imagers to realize face detection (FD). However, unlike using convolutional neural networks (CNNs) with

👥 作者与机构

Tzu-Hsiang Hsu*, Guan-Cheng Chen*, Yi-Ren Chen, Chung-Chuan Lo,

Ren-Shuo Liu, Meng-Fan Chang, Kea-Tiong Tang, Chih-Cheng Hsieh National Tsing Hua University, Hsinchu, Taiwan *Equally Credited Authors (ECAs) Vision systems with