Rocket One signs one-year research agreement with Virginia Commonwealth University on SKYMRAM for AI chips
Summary
Rocket One has announced a one-year research agreement with Virginia Commonwealth University focused on SKYMRAM magnetic memory, which is designed to enhance the efficiency and radiation tolerance of computing hardware used in AI systems. This agreement is significant as advancements in magnetic memory like SKYMRAM could support the development of next-generation AI infrastructure, particularly in demanding environments such as space, defense, and edge computing, where traditional silicon-based technologies face limitations in power and reliability.
Tokens
$RKTO
Analysis
Rocket One: Rocket One Inc. (NASDAQ: RKTO) develops and commercializes AI semiconductor infrastructure, with a focus on nanomagnetic and spintronic technologies suited for orbital, high-radiation, and defense environments. The company rebranded from Hoth Therapeutics in May 2026 to align with its emphasis on the space economy and next-generation AI chip architectures. Its new one-year research agreement with Virginia Commonwealth University targets SKYMRAM magnetic memory to advance AI chip capabilities. Virginia Commonwealth University: Virginia Commonwealth University is a public research university in Richmond, Virginia, with established expertise in engineering and advanced materials research. Its researchers have developed skyrmion-mediated MRAM (SKYMRAM) approaches aimed at energy-efficient, non-volatile memory solutions. The one-year research agreement with Rocket One applies this expertise directly to magnetic memory innovations for AI chips. Sector Relevance: Magnetic memory advancements support next-generation AI infrastructure in space, defense, and edge computing environments where traditional silicon approaches face power and reliability constraints. Technology Focus: SKYMRAM builds on skyrmion-based magnetic memory designs to enable more efficient, radiation-tolerant computing hardware for AI systems.
Categories
techmachine_learning
Related sources
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