Xu Minyi, Chen Xinrui, Guo Yehao, Wang Yang, Qiu Dong, Du Xinchuan, Cui Yi, Wang Xianfu, Xiong Jie
State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Adv Mater. 2023 Dec;35(51):e2301063. doi: 10.1002/adma.202301063. Epub 2023 Oct 30.
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
由于具有卓越的能源效率，神经形态计算一直备受关注，极有可能推动后摩尔时代的下一波通用人工智能发展。然而，目前的方法大多是为固定和单一任务设计的，因此在该领域会遇到连接困难、功耗以及数据密集型计算等问题。可重构神经形态计算是一种受大脑固有可编程性启发的按需范式，它可以最大限度地重新分配有限资源，以实现可重复的受大脑启发功能的扩展，凸显了一个弥合不同原语之间差距的颠覆性框架。尽管相关研究在具有新颖机制和架构的各种材料和器件中蓬勃发展，但精确的综述仍然空白且迫切需要。在此，从材料、器件和集成角度对这一研究方向的最新进展进行了系统综述。在材料和器件层面，全面总结了可重构性的主导机制，分为离子迁移、载流子迁移、相变、自旋电子学和光子学。还展示了可重构神经形态计算的集成层面发展。最后，讨论了可重构神经形态计算未来面临的挑战，明确拓展了其在科学界的视野。
