Project Grant N000142412633

This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports an initiative at Brown University to integrate machine learning (ML) techniques into electronic design automation (EDA) optimization algorithms. The goal is to improve the performance and speed of EDA tools, which are critical for designing and manufacturing computer chips. The project aims to develop numerical embeddings of chip...

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 $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 from the National Science Foundation's $1.2 million Computer and Information Science and Engineering program will fund the development of the Machine Learning Accelerator Cohort Architecture at the University of Southern California from October 2022 to September 2025. The ML Accelerator Cohort Architecture aims to build a heterogeneous computing fabric to efficiently support diverse machine learning models, including novel approaches for privacy-preserving computation. Key...

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...

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...

This $618,159 National Science Foundation project grant supports research into developing energy-efficient hardware and software for machine learning and artificial intelligence systems. Funded under the Computer and Information Science and Engineering program, the award supports The Washington University in investigating frameworks using quantum-tunneling dynamic-analog memory devices and novel online learning algorithms. Specific objectives include exploring Fowler-Nordheim dynamic analog...

The National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems awarded a $385,000 Project Grant to Northwestern University to develop cross-layer techniques from device to circuit and architecture for the large-scale integration of ferroelectric field effect transistors (FeFET) with complementary metal-oxide-semiconductor (CMOS) technology. This research aims to enable emerging computing applications, such as AI, robotics, augmented/virtual reality, and autonomous...

This Project Grant from the National Science Foundation will support the development of a hardware-aware machine learning platform tailored for resource-constrained industrial devices. Ai Pow LLC was awarded $275,889 under the NSF Technology, Innovation, and Partnerships program to create an automated model discovery platform that reduces manual efforts by generating complex machine learning models considering hardware speed and capacity constraints. The platform aims to produce advanced...

This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), aims to revolutionize artificial intelligence (AI) training through the development of a novel chip architecture called Co-FABPRO (Concurrent Forward and Backward Propagation). The $200,000 award, with a project period from October 1, 2024 to September 30, 2026, seeks to create a disruptive approach that will make AI...