Collaborative Research: FuSe: Metaoptics-Enhanced Vertical Integration for Versatile In-Sensor Machine Vision

合作研究：FuSe：Metaoptics 增强型垂直集成，实现多功能传感器内机器视觉

• 批准号: 2416375
• 负责人: Xuan Zhang
• 金额: $ 110万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-15 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2416375&HistoricalAwards=false
• 关键词: Collaborative; Research; FuSe; Metaoptics; Enhanced

Vision is perhaps the most important human perception, as the majority of the brain’s cognitive function is dedicated to processing visual information. Despite recent advancements, today’s vision sensors remain quite primitive when compared to the superior ability of human visual perception. Moreover, the rapid development of deep learning and artificial intelligence (AI) has unleashed a new wave of machine vision, where increasing amounts of image data are generated and consumed, not by humans, but by edge devices to perform intelligent tasks such as classification, recognition, and perception. Inspired by the biological system and motivated by the huge demand of machine vision, this project investigates an integrated and holistic approach to building versatile vision systems that can be tailored for domain-specific tasks. It aims to create a vertically-integrated design stack for vision sensors across optics, image sensors, and vision processors. The project is expected to herald a new paradigm of AI-driven vision systems and demonstrate technology to address pivotal engineering challenges from real-time visual adaptivity in self-driving cars to near-zero energy efficiency in persistent environmental monitoring. In addition, the project’s education and workforce development activities foster an open-source hardware community to boost accessibility and deepen collaboration beyond the traditional discipline divides, as well as to build up the capacity of domestic talents in vision sensor industry, critical to national security and supply chain safety. The research objective of this project is to create the scientific and engineering foundations for a novel machine vision system that explores the hybrid integration of nanophotonic metamaterials and complementary metal-oxide semiconductor (CMOS) circuits and synergistically leverages the intrinsic computing capability of computational metasurface and analog-domain encoder embedded inside the image sensors. Our principled approach to abstracting design knobs and modeling interactions and tradeoffs across the system layers and physical domains will inform future “More than Moore” multi-physics semiconductor device integration. We will delve into the key concept of optimally distributing computation along the processing pipeline with complementary intrinsic physical-domain operations. Our end-to-end design framework is deliberately created to bridge the divide between modeling/simulation infrastructure and design toolchains across multiple heterogeneous physical domains. The core principles of embedding machine-learning-enabled feature selection with optical/electrical vertical integration could have a major impact on the design of sensor-rich intelligent physical platforms where resource constraints coincide with strict latency requirements. The technology developed in this project will turbocharge AI-enabled hardware to satisfy the tremendous computational demand imposed by data proliferation, broadly benefiting a range of burgeoning industries such as machine vision as a service, smart IoT infrastructure, data-driven sensing and imaging.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

视觉可能是人类最重要的感知，因为大脑的大部分认知功能都致力于处理视觉信息。尽管最近取得了进步，但与人类视觉感知的上级能力相比，今天的视觉传感器仍然非常原始。此外，深度学习和人工智能（AI）的快速发展引发了新一轮机器视觉浪潮，越来越多的图像数据不是由人类生成和消费，而是由边缘设备生成和消费，以执行分类、识别和感知等智能任务。受生物系统的启发和机器视觉的巨大需求的激励，该项目研究了一种集成和整体的方法来构建可针对特定领域任务量身定制的多功能视觉系统。它旨在为光学、图像传感器和视觉处理器的视觉传感器创建垂直集成的设计堆栈。该项目预计将预示着人工智能驱动的视觉系统的新范式，并展示解决关键工程挑战的技术，从自动驾驶汽车的实时视觉适应性到持续环境监测中的近零能效。此外，该项目的教育和劳动力发展活动促进了开源硬件社区的发展，以提高可访问性并深化传统学科划分之外的合作，并建立了视觉传感器行业国内人才的能力，这对国家安全和供应链安全至关重要。该项目的研究目标是为新型机器视觉系统创建科学和工程基础，该系统探索纳米光子超材料和互补金属氧化物半导体（CMOS）电路的混合集成，并协同利用嵌入图像传感器内部的计算元表面和模拟域编码器的固有计算能力。我们的原则性方法，抽象设计旋钮和建模的相互作用和权衡系统层和物理域将告知未来的“超过摩尔”多物理半导体器件集成。我们将深入研究的关键概念，最佳分配计算沿着处理管道与互补的内在物理域操作。我们的端到端设计框架是特意创建的，以弥合建模/仿真基础设施和跨多个异构物理域的设计工具链之间的鸿沟。将支持机器学习的特征选择与光学/电气垂直集成相结合的核心原则可能会对传感器丰富的智能物理平台的设计产生重大影响，其中资源限制与严格的延迟要求相一致。该项目开发的技术将为支持AI的硬件提供动力，以满足数据激增带来的巨大计算需求，广泛惠及一系列新兴行业，如机器视觉即服务、智能物联网基础设施、数据-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Invited Paper: Learned In-Sensor Visual Computing: From Compression to Eventification

特邀论文：学习传感器内视觉计算：从压缩到事件化

• DOI: 10.1109/iccad57390.2023.10323842
• 发表时间: 2023
• 期刊: 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD
• 作者: Feng, Yu;Ma, Tianrui;Boloor, Adith;Zhu, Yuhao;Zhang, Xuan
• 通讯作者: Zhang, Xuan