This $450,000 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) aims to transform the process of designing computer hardware through a novel approach to electronic design automation (EDA). The research project at Cornell University will develop differentiable hardware synthesis techniques that combine formal methods, machine learning, and parallel computing to optimize circuit design in a faster, less...
This Project Grant award of $450,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop novel approaches to electronic design automation (EDA) for creating high-performance and efficient computer hardware.
The research introduces a strategy that combines formal techniques with learning-based optimization to enable differentiable hardware synthesis, particularly suited for heterogeneous computing. This new...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program Grant (CFDA 47.070) award of $214,364 to the Regents of the University of Minnesota aims to develop efficient and scalable computing paradigms for large graph machine learning in electronic design automation (EDA). The key products and services to be delivered under this 4.5-year project include:
An algorithm-hardware co-optimized paradigm that leverages EDA domain knowledge, graph...
This $127,484 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop an efficient compilation and synthesis flow for translating high-level programs into domain-specific reconfigurable heterogeneous acceleration systems. The key innovations include:
A heterogeneous hardware acceleration system template combining hardened digital accelerators, reconfigurable digital logic, and...
This $800,000 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to pioneer a computing system for massive AI workloads. The project will develop new architectural and design automation tools for a reconfigurable-on-package system using "tiny chiplets" - miniaturized composable computing components. This innovative approach seeks to address the challenges related to chiplet definition,...
This $599,963 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research at the University of Southern California (USC) to explore a new mathematical lens for understanding machine learning. The project aims to unlock insights into key questions around machine learning, such as what is learnable, the characteristics of optimal learning algorithms, and how to better enable learning. The...
This federal Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to revolutionize artificial intelligence (AI) training by developing a novel chip architecture that will make the training process dramatically faster and more energy-efficient.
The $200,000 award, with a performance period from October 1, 2024 to September 30, 2026, will fund the planning and feasibility studies for this new...
This $131,959 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research conducted by Rutgers, The State University to develop new deep learning training methods that can efficiently scale to utilize high-performance computing (HPC) systems. The key goals are to: 1) Explore techniques like second-order information approximation, computation-communication tradeoffs, and data compression to enhance the speed...
The University of California, Santa Barbara (UCSB) was awarded a $599,997 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program. The funding will support the development of innovative machine learning technology to address productivity and quality challenges in integrated circuit (IC) design and manufacturing. The project aims to create a semi-supervised learning framework to enable data-efficient circuit optimization,...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant, titled "CAREER: ADAPTIVE DEEP LEARNING SYSTEMS TOWARDS EDGE INTELLIGENCE," provides $249,028 to the University of Massachusetts (UMass) to develop adaptive deep learning systems for edge computing platforms. The goal is to enable the effective deployment of deep learning techniques across diverse applications and environments by optimizing both system efficiency and...