In recent years, the exceptional computational power of software-based deep learning has gained widespread recognition, fueling the rapid advancement of artificial intelligence (AI) applications across diverse fields. However, the continual scaling of silicon CMOS technology—central to current computing systems—is approaching physical limits, posing a significant barrier to further performance improvements. As a result, there is growing interest in alternative hardware paradigms capable of sustaining future progress in AI. Among them, artificial neural networks (ANNs) are driving the development of neuromorphic hardware, particularly in the form of reservoir computing devices, to address energy efficiency challenges.
These next-generation AI systems depart fundamentally from conventional computing architectures by leveraging dynamic physical properties rather than static logic circuits. A key innovation lies in exploiting the intrinsic nonlinearity and interconnectedness of nanomaterials to engineer devices that naturally exhibit pulses, noise, and other complex physical phenomena. Such behaviors are essential for enabling real-time learning, adaptation, and decision-making, while also dramatically lowering power consumption and enhancing integration density.
A central requirement for learning in ANNs is the dynamic modulation and storage of synaptic weights, typically realized through weighted summation operations. To meet this need, our research center has been actively developing novel materials and device architectures that complement—or potentially outperform—traditional CMOS-based systems. In particular, we focus on molecular systems and carbon nanotubes (CNTs) as promising candidates for building energy-efficient, scalable AI hardware.
This work highlights our recent progress in realizing CNT-based reservoir computing devices. We explore critical aspects of their operation, including the ability to generate time-dependent responses and strategies to ensure stability and reproducibility in complex nanomaterial networks. Notably, we demonstrate a system in which pressure sensor signals from a robotic hand are input into a 2D random CNT network, enabling accurate classification of grasped objects [1]. Furthermore, we show that transforming a CNT network into a 3D sponge-like structure not only haptic sensing capabilities but also imparts physical reservoir computational functionality [2].
Beyond device-level innovations, we are applying these technologies to build autonomous AI robots. By embedding nanomaterial-based systems into robotic platforms, we aim to demonstrate intelligent behavior that emerges from the underlying physical computation, rather than from software algorithms alone. These robots serve as proof-of-concept for the potential of neuromorphic hardware in real-world applications such as sensor fusion, adaptive control, and embodied intelligence.
In the presentation, I will also review our latest research findings, emphasizing the interplay between material properties, device-level dynamics, and system performance [3]–[10]. We provide a comprehensive overview of our approach, including the material engineering, device design, and integration strategies that underpin our progress. Taken together, our results underscore the transformative potential of nanoscale physical phenomena to power the next generation of intelligent systems—paving the way for truly energy-efficient, adaptive, and integrated AI hardware systems.