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.

现代计算机的应用程序范围需要快速，大规模的算术。认知计算机（C2M）' - 通过利用最近的OFREPR ESENTS的进步，计算信息和关键的见解，以将大脑结构的特征映射到三维综合芯片上，这是一种大脑结构的灵感。任务。然后，这些关键的海马功能将通过最先进的硬件设计技术来启用固态芯片。环境。在该项目中涉及的培训将在神经科学和工程轨迹上进行跨学科培训。在人类中建立和构建C。Ontext的日常生活，但对硬件和软件系统构成了重要的夏语从海马的认知功能启发了计算机芯片（认知计算机或C2M）。 ，能够复杂的动态类似物片处理，（3）在理解海马机制和动力学方面的进步，支持学习，记忆和决策。了解对应用的认知算法。 ND（3）算法抽象中的3D集成电路中的海马属性，就像将基于事件的基于事件的预测与基于知识的预测合并为基于事件的预测。和更广泛的影响审查标准。