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看法，因为大脑的认知功能大部分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卓越能力相比，当今的视力传感器仍然非常原始。此外，深度学习和人工智能（AI）的快速发展已被释放出新的机器视觉浪潮，其中越来越多的图像数据是由人类生成和消耗的，而是通过边缘设备来执行智能任务，例如分类，识别和感知。受生物系统的启发，并受到机器视觉需求的巨大需求的启发，该项目研究了一种综合而整体的方法来建立多功能视觉系统，该方法可以针对特定于领域的任务进行量身定制。它旨在为跨光学，图像传感器和视觉处理器垂直整合的设计堆栈创建一个垂直整合的设计堆栈。预计该项目将预示着AI驱动的视觉系统的新范式，并展示了从自动驾驶汽车的实时视觉适应性到接近零的能源效率的技术挑战，以应对持续的环境监测。此外，该项目的教育和劳动力发展活动促进了一个开源硬件社区，以提高可访问性并加深协作超出传统学科的鸿沟，并建立国内人才在视觉传感器行业中对国家安全和供应链安全至关重要的能力。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，该系统探讨了纳米光材料超材料和互补的金属氧化物半导体（CMOS）电路的混合整合，并协同利用了计算上的上层和模拟域的固有计算能力。我们在系统层和物理域上抽象设计旋钮以及建模相互作用和权衡的主要方法将为未来提供“超过摩尔”多物理学半导体设备的集成。我们将深入研究沿处理管道最佳分配计算的关键概念，并使用互补的内在物理域操作。我们故意创建了我们的端到端设计框架，以弥合建模/仿真基础架构与设计工具之间在多个异构物理领域之间进行的鸿沟。具有光学/电气垂直整合的嵌入机器学习功能选择的核心原理可能会对富含传感器的智能物理平台的设计产生重大影响，在这些设计中，资源约束与严格的延迟要求一致。该项目中开发的技术将启用涡轮增压硬件，以满足数据增殖所施加的巨大计算需求，从而使一系列新兴行业（如机器视觉，智能的IoT基础架构，数据驱动的敏感性和成像）受益于众多奖励，这反映了NSF的众多启发，这表明了NSF的法定任务和成像，从而受益于机器视觉，智能物联网基础架构和成像。 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