AI hardware needs to become more brain-like to meet the growing energy demands of real-world applications, according to researchers from Purdue University and Georgia Institute of Technology.
In their new Frontiers in Science lead article , Prof Kaushik Roy and Prof Arijit Raychowdhury present a novel approach to AI hardware design—integrating neuromorphic systems processing capabilities and compute-in-memory (CIM) techniques—to overcome the limitations of modern computing hardware. The article outlines a comprehensive roadmap for future AI-hardware research, emphasizing hardware–algorithm co-design to accelerate innovation across sectors such as healthcare, transportation, and robotics.
Join the authors at our Frontiers in Science Deep Dive webinar on 12 February 2026 , 16:00–17:30 CET , as they discuss emerging strategies that could reduce data center energy use and enable real-time intelligence in compact, power-constrained systems. Potential applications include on-device medical diagnostics, autonomous vehicles, and drones that navigate safely.
Breaking the memory wall: next-generation artificial intelligence hardware | 12 February 2026 | Register
Frontiers in Science Deep Dive sessions bring researchers, policy experts, and innovators together from around the world to discuss a specific area of transformational science published in Frontiers' flagship, multidisciplinary journal, Frontiers in Science, and explore next steps for the field.