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

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

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.