Yoon Chansoo, Oh Gwangtaek, Park Bae Ho
Division of Quantum Phases & Devices, Department of Physics, Konkuk University, Seoul 05029, Korea.
Nanomaterials (Basel). 2022 May 18;12(10):1728. doi: 10.3390/nano12101728.
As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.
随着人工智能和物联网的出现，数据量呈指数级增长，迫切需要具有高能源效率、高可扩展性和高处理速度的计算系统。与受冯·诺依曼瓶颈困扰的传统数字计算不同，受脑启发的计算可以基于神经元之间突触连接的模拟变化提供高效、并行和低功耗的计算。受脑启发的计算中的突触节点通常由数十个硅晶体管实现，这是一种能源密集型且不可扩展的方法。用于受脑启发计算的基于离子运动的突触器件，因其面积小和能源成本低，在模拟人类大脑中生物突触的性能方面受到越来越多的关注。本文讨论了用于受脑启发计算硬件实现的基于离子运动的突触器件的最新进展及其工作原理。从受脑启发计算的器件级要求的角度出发，我们阐述了不同类型的基于离子运动的突触器件的优势、挑战和未来前景。
