Project Grant 1951113

This $255,999 National Science Foundation award under the Technology, Innovation, and Partnerships program will support Zenoleap LLC's development of novel superconducting neuromorphic computing circuits. The awardee will design, fabricate, and characterize atomic-tunable memristors and superconducting quantum interference device neurons to enable true biological brain-inspired deep learning network algorithms. This superconducting neuromorphic circuit aims to significantly improve energy...

This $305,999 federal Project Grant award from the National Science Foundation's Engineering (CFDA 47.041) program supports the development of a novel hybrid CMOS+X-based in-memory analog computing framework. The project aims to create an energy-efficient neuromorphic computing system by leveraging industry-scale 3D NAND flash memory chips and academic laboratory-scale molecular memristors to emulate the human brain's remarkable efficiency. The framework will utilize 3D NAND flash memory to...

Wolfzyc LLC was awarded a $255,646 Project Grant from the National Science Foundation under the NSF Technology, Innovation, and Partnerships program to develop a neuromorphic computing platform using superconducting neural units with adjustable weights. This Small Business Innovation Research Phase I award will support the company's efforts over 11 months to advance the technological frontier with a high-speed superconductor electronics-based neuromorphic computer centered around a disordered...

This SBIR Phase I award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) provides $304,900 to Magnolia Electronics Inc. to develop a machine-learning enabled analog-to-digital converter (ADC). The project aims to demonstrate a new patent-pending approach to ADCs that can capture more information than traditional designs, with potential applications in high-speed data transmission for computer networks and data centers. The project...

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 SBIR Phase I award from the National Science Foundation's (NSF) Technology, Innovation, and Partnerships (TIP) program, with a total funding amount of $274,985, is focused on developing an artificial intelligence (AI) system to accelerate semiconductor production using physics-embedded lithographic foundation models. The project aims to alleviate design and manufacturing complexities associated with the shift to extreme ultraviolet (EUV) lithography in semiconductor manufacturing, which has...

The National Science Foundation (NSF) awarded a $266,000 Project Grant to the Georgia Tech Research Corporation, doing business as the Office of Sponsored Programs, under the Computer and Information Science and Engineering program (CFDA 47.070). The grant supports the development of a compact and energy-efficient "compute-in-memory" accelerator for deep learning applications leveraging ferroelectric vertical NAND memory technology. Key objectives include designing and evaluating...

This $500,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program supports a collaborative research effort between Yale University and its partners to develop energy-efficient algorithms and hardware for spike-based edge computing. The project aims to integrate spiking neural networks (SNNs), a brain-inspired computing paradigm, with modern integrated circuits to enable practical deployment of neuromorphic...

This $250,000 Project Grant, awarded by the National Science Foundation's Computer and Information Science and Engineering (CISE) program, supports a collaborative research effort focused on developing formal methods to synthesize and verify in-memory computing systems for neural networks. The project aims to: Verify the reliability of analog and digital in-memory computing (IMC) circuits used to accelerate neural networks, and Leverage machine learning and formal methods to synthesize...