基于连续时间数据表示的可扩展大规模并行计算
Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array
摘要：
物联网中联网智能设备的增长迫切需要实时处理和理解大量模拟数据。提高计算速度的困难使得数字计算无法满足处理在幅度和时间上本质上连续的模拟信息的需求。通过利用纳米级交叉杆阵列中的连续数据表示，可以实现并行计算以用于实时地直接处理模拟信息。在这里，我们提出了一个可扩展的大规模并行计算方案，利用连续时间的数据表示和频率复用的纳米级交叉阵列。这种计算方案使存储数据的并行读取和矩阵矩阵乘法的一次操作在交叉阵列。此外，我们实现了基于两个物理互连的交叉杆阵列的16个字母图像的一次性识别，并证明了在忆阻交叉杆阵列中可以同时进行模拟信息的处理和调制。
英文摘要：
The growth of connected intelligent devices in the Internet of Things has created a pressing need for real-time processing and understanding of large volumes of analogue data. The difficulty in boosting the computing speed renders digital computing unable to meet the demand for processing analogue information that is intrinsically continuous in magnitude and time. By utilizing a continuous data representation in a nanoscale crossbar array, parallel computing can be implemented for the direct processing of analogue information in real time. Here, we propose a scalable massively parallel computing scheme by exploiting a continuous-time data representation and frequency multiplexing in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix-matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar arrays and demonstrate that the processing and modulation of analogue information can be simultaneously performed in a memristive crossbar array.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
