Collaborative Research: NCS-FR: DEJA-VU: Design of Joint 3D Solid-State Learning Machines for Various Cognitive Use-Cases

合作研究：NCS-FR：DEJA-VU：针对各种认知用例的联合 3D 固态学习机设计

• 批准号: 2319617
• 负责人: Akhilesh Jaiswal
• 金额: $ 75万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319617&HistoricalAwards=false
• 关键词: Collaborative; Research; NCS; FR; DEJA

Modern computers have revolutionized a wide range of applications that require fast, large-scale arithmetic operations. However, today’s computers still perform poorly on ‘cognitive’ tasks – tasks that require knowledge building, learning from past experiences, and anticipating the future. This project aims to design a new class of computer chips – ‘Cognitive Computing Machines (C2M)’ – by leveraging advances in recent understanding of how the brain represents and computes information and the crucial insight to map complex signal routing, characteristic of brain structures, onto three-dimensional integrated chips. The inspiration for the project is the hippocampus, a brain structure known to be critical for performing these cognitive tasks. The project will model and quantify key information processing steps in the hippocampus. These key hippocampal functions will then be embedded on to solid-state computing chips through state-of-the-art hardware design techniques. A hippocampal-aware, hardware-aware, algorithmic framework will augment the chip design efforts to enable online learning and decision-making in resource constrained environments. The project has potential disruptive applications in the field of robotics and autonomous systems spanning industrial, consumer and defense sectors. Each participating investigator and institution are committed to support a wide range of training and mentoring programs, with a focus on students from groups underrepresented in science. Trainees involved in the project will receive rare cross-disciplinary training in neuroscience and engineering, providing a foundation for a wide variety of career trajectories. Further, the participating laboratories will disseminate project outcomes through scientific articles, public and conference presentations, and other outreach tools including project websites and joint curricular activities.The cognitive ability to use information from individual events to build knowledge and make context-appropriate decisions is integral to daily life in humans but poses a significant challenge for hardware and software systems. Decades of research has indicated that a brain structure called the hippocampus plays a crucial role in enabling context-appropriate decision-making. The goal of this project is to design a new class of computer chips (Cognitive Computing Machines or C2M) inspired from the cognitive functions of the hippocampus. The project leverages three significant and timely developments: (1) three-dimensional integration of chips, enabling novel routing techniques for spatio-temporal signals, (2) processing-in-memory technology, capable of complex dynamic analog on-chip processing, and (3) advances in the understanding of hippocampal mechanisms and dynamics supporting learning, memory, and decisions. Further, the designed in silico C2M will be augmented with rapid and robust decision-making through novel hippocampus-aware, hardware-aware learning algorithm for range of cognitive applications. The transformative potential of the project emerges from research conducted at three different levels of abstractions (threads) and directed towards a common goal: (1) neuroscience abstraction, as in identifying and answering key questions about organizational and functional principles of hippocampus; (2) hardware abstraction, as in functionally mimicking hippocampal computing attributes in 3D integrated circuits in a technology-friendly manner; and (3) algorithm abstraction, as in incorporating event-based predictions from the hippocampus-inspired chip with knowledge-based predictions for rapid and robust learning.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.

现代计算机已经彻底改变了需要快速、大规模算术运算的广泛应用，但是，今天的计算机在“认知”任务（需要构建知识、学习过去的经验和预测未来的任务）方面仍然表现不佳。旨在设计一种新型计算机芯片——“认知计算机器（C2M）”——利用最近对大脑如何表示和计算信息的理解的进展以及将复杂信号路由、大脑结构特征映射到该项目的灵感来自于海马体，这是一种对于执行这些认知任务至关重要的大脑结构，该项目将对海马体中的关键信息处理步骤进行建模和量化，然后将这些关键的海马体功能嵌入其中。通过最先进的硬件设计技术来实现固态计算芯片，海马感知、硬件感知的算法框架将增强芯片设计工作，从而在资源有限的环境中实现在线学习和决策。有每个参与的研究人员和机构都致力于支持广泛的颠覆性培训和指导计划，重点关注来自科学领域代表性不足的群体的学生。该项目的参与者将接受神经科学和工程学方面罕见的跨学科培训，为各种职业轨迹奠定基础。此外，参与实验室将通过科学文章、公开和会议演讲以及包括项目在内的其他推广工具来传播项目成果。网站和联合课程活动。使用来自个体事件的信息来构建知识并做出适合情境的决策的认知能力是人类日常生活不可或缺的一部分，但对硬件和软件系统提出了重大挑战。数十年的研究表明，称为“大脑结构”的大脑结构。海马体在实现适合情境的决策方面发挥着至关重要的作用。该项目的目标是设计一种受大脑认知功能启发的新型计算机芯片（认知计算机或 C2M）。该项目利用了三个重要且及时的进展：(1) 芯片的三维集成，为时空信号提供了新颖的路由技术，(2) 内存处理技术，能够进行复杂的动态模拟片上处理。 (3) 对支持学习、记忆和决策的海马机制和动力学的理解取得进展。此外，通过新颖的海马感知、快速、稳健的决策，将增强计算机设计的 C2M。该项目的变革潜力来自于在三个不同抽象层次（线程）进行的研究，并针对一个共同目标：（1）神经科学抽象，如识别和回答关键问题。关于海马体的组织和功能原理；(2) 硬件抽象，例如以技术友好的方式在功能上模仿 3D 集成电路中的海马计算属性；以及 (3) 算法抽象，例如结合基于事件的方法该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。