This $598,958 Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will support research at the University of Illinois to study the mechanical behavior of two-dimensional atomic sheets with defects. The project will leverage advances in artificial intelligence and machine learning to overcome computational challenges in modeling the elasticity, strength, and fracture properties of these two-dimensional lateral heterostructures. The research aims to...
This three-year, $349,772 project grant from the National Science Foundation's Engineering Directorate (ENG), program CFDA 47.041, supports collaborative research on two-dimensional ambipolar machine learning and logical computing systems at the University of Illinois. The researchers will develop new 2D ambipolar materials and device structures to design ambipolar logic families and machine learning architectures that leverage the unique behaviors of these dual-gate field-effect transistors....
This $2,000,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) to Northwestern University. The goal of the project is to develop electronic devices that emulate the cerebellar functions of the brain, such as anomaly detection, to enable more robust and energy-efficient systems in applications like cybersecurity, autonomous robotics, and power-delivery control. The research combines expertise from theoretical...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $1,200,000 to Purdue University to develop scalable and ultra-low-power neural accelerators based on 2D ferroelectric semiconductors. The research aims to address hardware needs for future artificial intelligence (AI) platforms by utilizing the unique properties of ferroelectric semiconductors to design energy-efficient circuits and...
The University of Illinois was awarded a $400,000 project grant from the National Science Foundation Division of Computing and Communication Foundations under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support collaborative research on developing scalable deep learning techniques through gate-tunable molybdenum disulfide (MoS2) crossbars from September 1, 2021 to August 31, 2025. The research aims to advance neuro-inspired computing...
This Project Grant award for $200,000 from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research by Southern Illinois University Carbondale (SIU) to develop hardware-constraint-aware design and optimization techniques for memristor-crossbar-array (MCA) based neural network accelerators. The research aims to bridge the gap between software design tools and the physical limitations of MCA hardware, enabling more efficient and cost-effective development of...
The National Science Foundation (NSF) Division of Information and Intelligent Systems awarded a $200,000 Project Grant to Temple University to develop transformative machine learning and data analytics technologies for enabling AI-based applications on resource-constrained edge computing devices. The project aims to address gaps between the complexity of data and the limited computing resources on edge devices, as well as the need for robust predictive models across heterogeneous edge...
The National Science Foundation (NSF) Directorate for Engineering (CFDA 47.041) awarded a $360,000 project grant to the Regents of the University of Michigan to conduct research advancing key knowledge and techniques for creating electronic devices for fabrication of future artificial intelligence systems. The grant supports fundamental research to explore critical device physics knowledge for the realization of new memristive switching devices (memristors) based on 2D nanomaterials, which could...
The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Arizona State University, Division (doing business as Orspa), for the project "COLLABORATIVE RESEARCH: SHF: MEDIUM: TINY CHIPLETS FOR BIG AI: A RECONFIGURABLE-ON-PACKAGE SYSTEM". This 4-year project (7/1/2024 - 6/30/2028) aims to pioneer a computing system for massive AI workloads, including new architectural and design automation...
This $340,000 National Science Foundation project grant supports research at the University of Texas at Austin to develop two-dimensional ambipolar materials and device structures for logical and machine learning circuits. The grant is part of NSF's $47 million Engineering program, which funds research and education activities to advance engineering innovations. Specifically, the researchers will study dual-gate ambipolar field-effect transistors made from two-dimensional materials to design new...
This National Science Foundation Engineering grant of $100,000 awarded on May 15, 2023 to the University of Illinois will fund the development of foundational semiconductor technologies and co-design methodologies for risk-aware inference at the edge using two-dimensional semiconductors. Key objectives include exploring novel device concepts based on 2D materials to enable Bayesian inference of deep neural networks with ultra-low power and robust autonomy for edge robotics applications. The university will develop a cross-layer simulation tool to bridge material innovations to computing architecture design and explore unconventional system concepts. It will also establish workshops and courses on emerging co-design methodologies to address workforce needs. As part of the effort, the grant will support the autonomous navigation of insect-scale drones as a test platform. Subawards totaling $30,000 were issued to Northwestern University to develop a training dataset for visual navigation of such drones and to the University of Washington to quantify robotic system elements, create associated models, and ensure integration with semiconductor models for system-level co-design.