Public Abstract

DE-SC0019273: Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C)

Award Status: Active

Institution: The Regents of the University of California - UCSD, La Jolla, CA
UEI: UYTTZT6G9DT1
DUNS: 804355790

Most Recent Award Date: 07/31/2025
Number of Support Periods: 8
PM: Holder, Aaron

Current Budget Period: 08/01/2025 - 07/31/2026
Current Project Period: 08/01/2022 - 07/31/2026
PI: Schuller, Ivan

Supplement Budget Period: N/A

Public Abstract

QUANTUM-MATERIALS FOR ENERGY EFFICIENT NEUROMORPHIC-COMPUTING (Q-MEEN-C) Principal Investigator: Ivan K. Schuller (University of California San Diego) Co-Investigators: Robert Dynes, Eric Fullerton, Oleg Shpyrko, Alex Frañó, Marcelo Rozenberg, Duygu Kuzum (University of California San Diego), Catherine Schuman (University of Tennessee), Giulia Galli (University of Chicago), Yayoi Takamura (University of California, Davis), Jonathan Schuller (University of California, Santa Barbara), Julie Grollier (CNRS), Andrew Kent (NYU), Axel Hoffmann (UIUC), Yimei Zhu (BNL), Shriram Ramanathan (Rutgers University) “Moore’s law” has fueled the large explosion in the use and manipulation of data in everyday life. It is commonly agreed that in the next 15-25 years a “Moore’s crisis” will develop, in which the continuous improvement in computational power and the decrease in cost will end. This project is dedicated to establishing the fundamental research from which the next big information revolution will arise, by answering the grand challenge: “Develop a computational machine that works like the brain” (“neuromorphic”). This device should be able to capture in an energy-efficient manner, the essence from complex inputs and be fault tolerant. Powerful interconnected computers and databases provide seemingly endless information and enable actions at a distance. The massive amount of information at our disposal poses new challenges. How to make sense of it? How to recognize patterns? How to make decisions from vast but often conflicting, incomplete, or imprecise data? How to take complex inputs and obtain from them simple qualitative understanding conclusions? Energy-efficient neuromorphic computing offers the potentially disruptive technological capability to process complex inputs and produce elegantly simple, useful outputs. The breakaway from conventional technology – the Turing-von Neumann paradigm – requires the development of new types of bio-inspired (“neuromorphic”) devices with completely new types of functionalities: artificial synapses, neurons, axons, and dendrites that can be used to construct machines with artificial-intelligence capabilities. The much needed, new, bio-inspired functionalities cannot be implemented using the conventional materials, devices and architecture. They are energetically inefficient, strongly affected by defects, and are about to reach the fundamental limits of size and speed. A stringent energy-efficiency requirement stems from the energetic consideration of existing neural systems, such as the human brain. The human brain requires about 10 Watts, and contains 10¹¹ neurons and 10¹⁴synapses, which leads to 0.1 picowatt per synapse. It is widely believed that novel “quantum” materials offer pathways to achieve these functionalities with a similarly high energy efficiency. The principal goal of Q-MEEN-C is to develop the basic understanding of quantum materials at hierarchical length scales that enable energy-efficient, fault/defect-tolerant, emergent networks to yield a materials basis for a neuromorphic computational paradigm. Quantum materials possess multiple properties at various length and time scales that can be used as a platform for energy-efficient neuromorphic phenomena and functionalities. To develop an understanding of which properties need to be optimized and to what extent, it is crucial to test these materials under realistic conditions. As a result, Q-MEEN-C is an integrated research effort influenced by and cognizant of the need for enhanced neuromorphic functionalities and the emergence of novel properties at the network scale. We address this challenge with a holistic approach that considers aspects of materials at various length scales, from the microscopic to the macroscopic, in the same way that the brain itself is understood as an emergent phenomenon. Q-MEEN-C will develop the next-generation energy-efficient quantum-materials-based platforms which could be the basis of neuromorphic computing. Potential Impact: The energy-efficient quantum materials, devices, and systems to be investigated will be key ingredients in the development of a neuromorphic machine that works like the brain. These discoveries will form the basis for the next transformative technological revolution in data manipulation, transmission, and storage. The development of quantum materials for next generation computing approaches pursued by Q-MEEN-C are expected to ameliorate the global energy problem posed by the explosive increase in data manipulation in the next 20 years.

QUANTUM-MATERIALS FOR ENERGY EFFICIENT NEUROMORPHIC-COMPUTING (Q-MEEN-C)

Principal Investigator: Ivan K. Schuller (University of California San Diego)

Co-Investigators: Robert Dynes, Eric Fullerton, Oleg Shpyrko, Alex Frañó, Marcelo Rozenberg, Duygu Kuzum (University of California San Diego), Catherine Schuman (University of Tennessee), Giulia Galli (University of Chicago), Yayoi Takamura (University of California, Davis), Jonathan Schuller (University of California, Santa Barbara), Julie Grollier (CNRS), Andrew Kent (NYU), Axel Hoffmann (UIUC), Yimei Zhu (BNL), Shriram Ramanathan (Rutgers University)

“Moore’s law” has fueled the large explosion in the use and manipulation of data in everyday life. It is commonly agreed that in the next 15-25 years a “Moore’s crisis” will develop, in which the continuous improvement in computational power and the decrease in cost will end. This project is dedicated to establishing the fundamental research from which the next big information revolution will arise, by answering the grand challenge: “Develop a computational machine that works like the brain” (“neuromorphic”). This device should be able to capture in an energy-efficient manner, the essence from complex inputs and be fault tolerant.

Powerful interconnected computers and databases provide seemingly endless information and enable actions at a distance. The massive amount of information at our disposal poses new challenges. How to make sense of it? How to recognize patterns? How to make decisions from vast but often conflicting, incomplete, or imprecise data? How to take complex inputs and obtain from them simple qualitative understanding conclusions? Energy-efficient neuromorphic computing offers the potentially disruptive technological capability to process complex inputs and produce elegantly simple, useful outputs. The breakaway from conventional technology – the Turing-von Neumann paradigm – requires the development of new types of bio-inspired (“neuromorphic”) devices with completely new types of functionalities: artificial synapses, neurons, axons, and dendrites that can be used to construct machines with artificial-intelligence capabilities.

The much needed, new, bio-inspired functionalities cannot be implemented using the conventional materials, devices and architecture. They are energetically inefficient, strongly affected by defects, and are about to reach the fundamental limits of size and speed. A stringent energy-efficiency requirement stems from the energetic consideration of existing neural systems, such as the human brain. The human brain requires about 10 Watts, and contains 10¹¹ neurons and 10¹⁴synapses, which leads to 0.1 picowatt per synapse. It is widely believed that novel “quantum” materials offer pathways to achieve these functionalities with a similarly high energy efficiency.

The principal goal of Q-MEEN-C is to develop the basic understanding of quantum materials at hierarchical length scales that enable energy-efficient, fault/defect-tolerant, emergent networks to yield a materials basis for a neuromorphic computational paradigm. Quantum materials possess multiple properties at various length and time scales that can be used as a platform for energy-efficient neuromorphic phenomena and functionalities. To develop an understanding of which properties need to be optimized and to what extent, it is crucial to test these materials under realistic conditions. As a result, Q-MEEN-C is an integrated research effort influenced by and cognizant of the need for enhanced neuromorphic functionalities and the emergence of novel properties at the network scale.

We address this challenge with a holistic approach that considers aspects of materials at various length scales, from the microscopic to the macroscopic, in the same way that the brain itself is understood as an emergent phenomenon. Q-MEEN-C will develop the next-generation energy-efficient quantum-materials-based platforms which could be the basis of neuromorphic computing.

Potential Impact: The energy-efficient quantum materials, devices, and systems to be investigated will be key ingredients in the development of a neuromorphic machine that works like the brain. These discoveries will form the basis for the next transformative technological revolution in data manipulation, transmission, and storage. The development of quantum materials for next generation computing approaches pursued by Q-MEEN-C are expected to ameliorate the global energy problem posed by the explosive increase in data manipulation in the next 20 years.