Rocket One has signed a research agreement with Virginia Commonwealth University (VCU) to develop ultra-low-power magnetic computing devices that could reduce the energy demands of AI processors, as the industry faces mounting pressure to deliver greater computational performance without driving up electricity consumption.<\/p>\n\n\n\n
The agreement establishes a one-year fabrication and testing program focused on straintronic magnetic tunnel junctions – anoscale devices that use voltage-induced mechanical strain to control magnetic behavior. The program targets foundational components for next-generation AI chips and accelerators, combining compact design, energy-efficient computation and the ability to retain stored states without continuous power.<\/p>\n\n\n\n
The project is led by VCU Professor Supriyo Bandyopadhyay, Ph.D., with planned device-fabrication collaboration involving Professor Jian-Ping Wang of the University of Minnesota, identified in the project plan as a subcontractor.<\/p>\n\n\n\n
“AI’s continued advancement depends on hardware that can deliver more computation with less energy,” said Robb Knie, CEO, Rocket One. “This agreement establishes a concrete fabrication and testing program targeting magnetic devices for next-generation AI chips. We see potential across AI infrastructure and autonomous systems, including defense and space applications where power consumption, size and local processing capabilities are critical.”<\/p>\n\n\n\n
Ultra-low-power analog multiplication:<\/strong> Fabricate and test a magnetic device that multiplies two voltage inputs, addressing a fundamental mathematical operation used in AI and machine learning.<\/p>\n\n\n\n Devices for more accurate AI matrix computation:<\/strong> Demonstrate a magnetic crosspoint device with continuously tunable, linear electrical conductance and investigate ways to expand its operating range. This behavior is intended to support more accurate computation in crossbar-based AI hardware.<\/p>\n\n\n\n Compact magnetic logic:<\/strong> Fabricate and demonstrate an exclusive-OR, or XOR, logic gate based on a single straintronic magnetic tunnel junction. XOR functions are used in arithmetic, error detection and encryption.<\/p>\n\n\n\n The research plan cites theoretical estimates of energy consumption as low as 100 attojoules per multiplication operation for the proposed magnetic multiplier. The program will focus on fabricating devices and measuring their operation experimentally. This figure is a theoretical device-level estimate, rather than measured performance from the announced program or a complete AI processor.<\/p>\n\n\n\n The proposed devices are also non-volatile, meaning their stored magnetic states can persist without continuous power. These characteristics could support future in-memory computing architectures that bring computation closer to stored information.<\/p>\n\n\n\n “Our objective is to translate promising magnetic computing designs into measurable device results,” Knie said.”Those results would help us assess performance, define the next engineering milestones and evaluate potential commercialization pathways.”<\/p>\n\n\n\n Potential applications include autonomous platforms, remote sensing and defense and space systems that must process information locally. The announced program focuses on foundational device fabrication and electrical testing. Processor integration and qualification for specific applications, including space operating environments, would require additional development.<\/p>\n\n\n\n It grants Rocket One an option to negotiate licenses to intellectual property arising from the project, including the ability to seek an exclusive license within its field of commercial interest, subject to the agreement’s terms, VCU’s ability to grant those rights and a separately negotiated license.<\/p>\n","protected":false},"excerpt":{"rendered":" Rocket One has signed a research agreement with Virginia Commonwealth University (VCU) to develop ultra-low-power magnetic computing devices that could reduce the energy demands of AI processors, as the industry faces mounting pressure to deliver greater computational performance without driving up electricity consumption. 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