Shen Jiabin, Cheng Zengguang, Zhou Peng
State Key Laboratory of ASIC and Systems, School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China.
Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, People's Republic of China.
Nanotechnology. 2022 Jun 20;33(37). doi: 10.1088/1361-6528/ac723f.
As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.
随着人工智能的持续快速发展,主流计算硬件在处理海量数据(大数据)时不可避免地面临挑战。基于冯·诺依曼架构、处理器单元与内存分离的传统计算机系统正接近计算速度和能源效率的极限。因此,基于新兴存储技术的新型计算架构,如内存计算和神经形态计算,被提了出来。近年来,光被融入计算设备中,这超出了传统光通信中的数据传输范畴,因为光在速度、带宽、能源效率等方面具有先天优势。其中,光辅助和光电突触被开发用于神经形态计算。此外,在一些新兴光子器件中,存储和读出过程都可以在光域中实现,以利用光子学的独特特性。在本综述中,我们介绍了源自新兴存储技术的典型光子神经形态器件及其相应的运行机制。最后,列出并讨论了这些源于不同调制方式的器件的优缺点。