Rocket One has announced a sponsored research agreement with Virginia Commonwealth University (VCU) to develop and demonstrate magnetic computing devices aimed at a critical challenge in artificial intelligence: delivering more computational capability while using less energy.
The agreement sets up a one-year fabrication and testing program focused on straintronic magnetic tunnel junctions, which are nanoscale 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. It is led by VCU Professor Supriyo Bandyopadhyay, with planned device-fabrication collaboration involving Professor Jian-Ping Wang of the University of Minnesota, who is identified in the project plan as a subcontractor.
Robb Knie, Chief Executive Officer of Rocket One, said AI's continued advancement depends on hardware that delivers more computation with less energy. He described the agreement as a concrete fabrication and testing program for magnetic devices aimed at next-generation AI chips, and said the company sees potential across AI infrastructure and autonomous systems, including defense and space applications where power consumption, size, and local processing are critical.
The program will pursue three complementary hardware demonstrations. The first is ultra-low-power analog multiplication, in which a magnetic device multiplies two voltage inputs, a fundamental operation in AI and machine learning. The second is a magnetic crosspoint device with continuously tunable, linear electrical conductance, along with research into expanding its operating range, which is intended to support more accurate computation in crossbar-based AI hardware. The third is compact magnetic logic, namely an exclusive-OR (XOR) gate built from a single straintronic magnetic tunnel junction, since XOR functions are used in arithmetic, error detection, and encryption. Because matrix multiplication is a core operation in neural networks, devices that perform multiplication and support crossbar architectures could become building blocks for future AI accelerators.
The project builds on earlier straintronic magnetic tunnel junction research, including an experimental device demonstration involving VCU and University of Minnesota researchers. The research plan cites theoretical estimates of energy consumption as low as 100 attojoules per multiplication for the proposed magnetic multiplier. The company stresses that this is a theoretical device-level estimate rather than measured performance from the announced program or from a complete AI processor, and that the program itself will focus on fabricating devices and measuring their operation experimentally. The devices are also nonvolatile, meaning their magnetic states persist without continuous power, which could support future in-memory computing architectures that bring computation closer to stored data. Knie added that the goal is to translate promising magnetic computing designs into measurable device results that help assess performance, define the next engineering milestones, and evaluate commercialization pathways.
Rocket One sees compact, energy-efficient processing as strategically relevant to both AI infrastructure and systems operating under tight power budgets or limited connectivity, such as autonomous platforms, remote sensing, and defense and space systems that must process information locally. The announced program covers only foundational device fabrication and electrical testing, and processor integration and qualification for specific applications, including space environments, would require further development.
The research period runs from October 19, 2026, through October 19, 2027. The agreement gives 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.
