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: 2319619
• 负责人: Maryam Parsa
• 金额: $ 55万
• 依托单位: George Mason 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: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319619&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.

现代计算机已彻底改变了需要快速，大规模算术操作的广泛应用。但是，当今的计算机在“认知”任务上的表现仍然很差 - 需要知识建设，从过去的经验中学习以及预期未来的任务。该项目旨在通过在了解大脑如何代表和计算信息以及映射复杂信号路由的关键见解的情况下利用进步来设计新的计算机芯片 - “认知计算机（C2M）” - 绘制进步，并将其绘制到三维集成芯片上。该项目的灵感是海马，这是一种大脑结构，对于执行这些认知任务至关重要。该项目将建模并量化海马中的关键信息处理步骤。然后，这些关键的海马功能将通过最先进的硬件设计技术嵌入到固态计算芯片上。海马感知，硬件感知，算法框架将增加芯片设计工作，以在资源约束环境中实现在线学习和决策。该项目在跨越工业，消费者和国防领域的机器人技术和自主系统领域具有潜在的破坏应用。每个参与的调查员和机构都致力于支持广泛的培训和心理计划，重点是来自科学领域不足的团体的学生。参与该项目的学员将接受神经科学和工程学的罕见跨学科培训，为各种职业轨迹提供基础。此外，参与的实验室将通过科学文章，公共和会议演讲以及包括项目网站和联合当前活动在内的其他外展工具来传播项目成果。使用各个事件的信息来构建知识并做出上下文适当的决策是人类日常生活的重要性认知能力，但对硬件和软件系统构成了重大挑战。数十年的研究表明，一种称为海马的大脑结构在实现背景的决策方面起着至关重要的作用。该项目的目的是设计新的计算机芯片（认知计算机或C2M），灵感来自海马的认知功能。该项目利用了三个重大且及时的发展：（1）芯片的三维整合，为时空信号提供新颖的路由技术，（2）在内存技术中处理过程，能够进行复杂的动态模拟芯片处理处理，以及（3）对海马机制和动力学学习的理解的进步，并支持学习，记忆和记忆，并决定，并确定。此外，通过新颖的海马感知，硬件感知的学习算法，用于一系列认知应用程序的新型海马知识，可以通过快速，强大的决策来增强设计中的设计。该项目的变革潜力来自于三个不同级别的抽象（线程）进行的研究，并针对一个共同的目标：（1）神经科学抽象，例如确定和回答有关海马组织组织和功能原理的关键问题； （2）硬件抽象，就像在功能上以技术友好的方式模仿3D集成电路中的海马计算属性； （3）算法抽象，将基于事件的预测纳入了海马启发的芯片中的基于事件的预测以及基于知识的快速学习预测。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来诚实地通过评估来诚实地支持。