清华大学陈宏伟团队研究了基于内存光计算的超紧凑型多任务处理器。该研究于2025年3月24日发表在《光：科学与应用》杂志上。

为了提高片上神经形态硬件的计算密度和能量效率，研究组引入了一种用于内存光学计算的多任务处理的新型网络架构。片上光学神经网络因其在进行被动计算的同时将大量参数转换为光学形式的能力而闻名，但它们在可扩展性和多任务处理方面遇到了挑战。利用迁移学习的原理，这种方法涉及将大部分参数嵌入固定的光学元件中，将少数参数嵌入可调的电气元件中。此外，通过在物理传播过程建模中使用深度回归算法，紧凑的光学神经网络可以处理各种任务。

在这项工作中，使用深度神经网络模型和硬参数共享算法，制造了两个集成了60000多个参数/mm²的超紧凑内存衍射芯片，分别执行多方面的分类和回归任务。实验结果表明，这些芯片实现了与电气网络相当的精度，同时将功耗密集型数字计算显著降低了90%。该工作预示着推进内存光学计算框架和下一代人工智能平台的巨大潜力。

附：英文原文

Title: Ultra-compact multi-task processor based on in-memory optical computing

Author: Liu, Wencan, Huang, Yuyao, Sun, Run, Fu, Tingzhao, Yang, Sigang, Chen, Hongwei

Issue&Volume: 2025-03-24

Abstract: To enhance the computational density and energy efficiency of on-chip neuromorphic hardware, this study introduces a novel network architecture for multi-task processing with in-memory optical computing. On-chip optical neural networks are celebrated for their capability to transduce a substantial volume of parameters into optical form while conducting passive computing, yet they encounter challenges in scalability and multitasking. Leveraging the principles of transfer learning, this approach involves embedding the majority of parameters into fixed optical components and a minority into adjustable electrical components. Furthermore, with deep regression algorithm in modeling physical propagation process, a compact optical neural network achieve to handle diverse tasks. In this work, two ultra-compact in-memory diffraction-based chips with integration of more than 60,000 parameters/mm2 were fabricated, employing deep neural network model and the hard parameter sharing algorithm, to perform multifaceted classification and regression tasks, respectively. The experimental results demonstrate that these chips achieve accuracies comparable to those of electrical networks while significantly reducing the power-intensive digital computation by 90%. Our work heralds strong potential for advancing in-memory optical computing frameworks and next generation of artificial intelligence platforms.

DOI: 10.1038/s41377-025-01814-0

Source: https://www.nature.com/articles/s41377-025-01814-0