TY - JOUR
T1 - Multimodal sensing-computing devices
T2 - Toward a new paradigm for embodied intelligence
AU - Xu, Chenhui
AU - Zheng, Tong
AU - Xie, Xinkai
AU - Wang, Zhuoran
AU - Shi, Qiongfeng
AU - Shen, Guozhen
AU - Wu, Jun
N1 - Publisher Copyright:
© 2026 Elsevier Ltd.
PY - 2026/3/1
Y1 - 2026/3/1
N2 - As artificial intelligence systems advance in efficiency, power economy, and greater autonomy, the conventional separated architecture of sensing and computing modules can no longer meet the demands of real-time response in complex environments. Multimodal sensing-computing devices (MSCDs) offer a promising technological pathway for embodied intelligent systems, merging multi-source signal perception, data preprocessing, and neuromorphic computation within a single physical platform. This review systematically outlines recent advances in multimodal perception and neuromorphic computing, with a focus on the contrast between unimodal and multimodal perception mechanisms, as well as strategies for multimodal data fusion and decoupling. Furthermore, it explores the structural design and cross-modal coupling mechanisms of MSCDs. Representative applications of such integrated systems are also surveyed across various domains, including embodied intelligent robots, wearable electronics, bionic prosthetics, and multimodal scene recognition. By analyzing the advantages and limitations of existing technologies, this article identifies critical directions for achieving low power consumption, high integration, and adaptive learning capabilities. MSCDs not only provide new insights into the hardware realization of artificial intelligence but also lay the solid foundation for constructing embodied intelligent systems that can perceive, interpret, and co-evolve with their environments.
AB - As artificial intelligence systems advance in efficiency, power economy, and greater autonomy, the conventional separated architecture of sensing and computing modules can no longer meet the demands of real-time response in complex environments. Multimodal sensing-computing devices (MSCDs) offer a promising technological pathway for embodied intelligent systems, merging multi-source signal perception, data preprocessing, and neuromorphic computation within a single physical platform. This review systematically outlines recent advances in multimodal perception and neuromorphic computing, with a focus on the contrast between unimodal and multimodal perception mechanisms, as well as strategies for multimodal data fusion and decoupling. Furthermore, it explores the structural design and cross-modal coupling mechanisms of MSCDs. Representative applications of such integrated systems are also surveyed across various domains, including embodied intelligent robots, wearable electronics, bionic prosthetics, and multimodal scene recognition. By analyzing the advantages and limitations of existing technologies, this article identifies critical directions for achieving low power consumption, high integration, and adaptive learning capabilities. MSCDs not only provide new insights into the hardware realization of artificial intelligence but also lay the solid foundation for constructing embodied intelligent systems that can perceive, interpret, and co-evolve with their environments.
KW - Embodied intelligence
KW - Multimodal perception
KW - Neuromorphic computation
KW - Neuromorphic devices
UR - https://www.scopus.com/pages/publications/105029552771
U2 - 10.1016/j.mattod.2026.103223
DO - 10.1016/j.mattod.2026.103223
M3 - Article
AN - SCOPUS:105029552771
SN - 1369-7021
VL - 93
JO - Materials Today
JF - Materials Today
M1 - 103223
ER -