Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Abstract

Unprecedented advances in Artificial Intelligence (AI)-assisted automation continue to drive the demand for hardware that is significantly more scalable, compact, and energy-efficient. Neuromorphic electronics, which offers event-driven and massively parallel information handling capabilities inspired by the biological cognition, provides a compelling solution, especially as emerging device technologies now enable true in-memory computation and tightly integrated sensing capabilities far beyond what CMOS alone can achieve. To keep pace with rapid progress in AI algorithms, the discovery of new functional materials and their integration into unconventional computing architectures has become a critical research frontier. This perspective highlights the potential of various classes of device technologies for next-generation neuromorphic AI hardware, showcasing key breakthroughs in robust, flexible, and conformable device platforms. Such technologies are particularly promising for resource-constrained edge platforms, such as wearable electronics, soft robotics, and autonomous embedded sensing systems. Lastly, we discuss that circuit- and system-level design must advance alongside device innovation, including robust biasing schemes, reliable peripheral integration, and scalable architectures that can support dense neuromorphic arrays. Looking forward, the field must embrace full-stack co-optimization from materials and device physics to circuits, architectures, and learning algorithms, ultimately enabling adaptive, autonomous computing embedded seamlessly into everyday environments.