The National Science Foundation (NSF) awarded a $340,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Missouri System to develop sustainability-aware design methods for approximate deep neural network (DNN) accelerators. The key objectives of this 3-year project are: Investigating efficient neural architecture search techniques for reliable and sustainable approximate DNN accelerator designs. Developing fault mitigation and...
The National Science Foundation awarded a $1,247,506 Project Grant to the University of Texas at Dallas under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) for the period of October 1, 2022 through September 30, 2025. The grant funds research to comprehend and mitigate errors in analog implementations of on-die neural networks. Specifically, the university will investigate and develop methods to address the impact of manufacturing and operational...
This Project Grant award of $731,058, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, aims to design and evaluate an energy-efficient hardware/software framework for on-chip implementation of deep neural networks (DNNs). The primary objectives are to: (1) develop a radically innovative near-memory CMOS+X architecture to significantly reduce DNN energy consumption by 10-100x, and (2) customize this framework for new DNNs to enable...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $549,999 to the University of North Texas to develop a "Time-Sensitive Large Model Training Platform for Dynamic Data Analytics". The key goals are to create methods for dynamically refining and adapting large-scale deep learning models to changing conditions in real-time, reducing the need for time-consuming retraining. This will...
This $134,992 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program aims to develop methods and architectural support for energy-efficient and scalable artificial intelligence (AI) systems. The key objectives are to: Leverage dynamic connectivity to reduce redundancy in AI models by adapting them to specific tasks and data. Provide architectural support for elastic processing through heterogeneous architectures...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant awarded to North Carolina State University (NC State) provides $208,745 to develop scalable and stable neural network paradigms to address variability issues in emerging device-based platforms for large-scale neuromorphic computing. The project aims to improve the reliability and sustainability of deep learning accelerators for data centers by explicitly modeling weight uncertainties,...
This Project Grant award of $180,803 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support research into enabling the reuse, replacement, and independent evolution of deep neural network (DNN) modules. The research aims to address key challenges in the software engineering of DNN-based systems, such as explainability, scalability, and correctness. Specifically, the project will investigate approaches to systematically...
This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) to Northeastern University, totaling $211,129, aims to develop a design framework for efficient information processing that leverages insights from biological systems. The key objectives are to: Advance analog/mixed-signal (AMS) design automation and create novel neural network-inspired model abstractions and hardware substrates to enable a streamlined design flow...
The University of Texas at Austin was awarded a $450,000 Project Grant from the National Science Foundation (NSF) Division of Computing and Communication Foundations (CFDA 47.070 Computer and Information Science and Engineering) to conduct collaborative research on energy-efficient machine learning hardware for edge computing applications. The key research objectives are to: (1) develop novel weightless neural network architectures that combine the benefits of traditional deep neural networks...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant, award ID 2403723, provides $500,000.00 to Cornell University to develop novel algorithms and hardware designs for energy-efficient, memory-optimized spiking neural network (SNN) systems on edge computing devices. The project aims to advance the practical deployment of neuromorphic computing for applications like drones, autonomous robots, portable medical devices, and wearable...