Direct tensor processing with coherent light
Yufeng Zhang, Xiaobing Liu, Chenguang Yang, Jinlong Xiang, Hao Yan, Tianjiao Fu, Kaizhi Wang, Yikai Su, Zhipei Sun, and Xuhan Guo

TL;DR
This paper introduces POMMM, a novel optical computing method that enables fully parallel tensor processing using coherent light, significantly improving performance and scalability for neural network applications.
Contribution
The paper presents POMMM, a new optical paradigm for tensor processing that scales with data dimension and outperforms existing optical methods in efficiency and versatility.
Findings
High consistency with GPU-based matrix multiplication
Effective in neural network applications like CNNs and vision transformers
Supports multi-wavelength and large-scale expansion
Abstract
Tensor processing is the cornerstone of modern technological advancements, powering critical applications in data analytics and artificial intelligence. While optical computing offers exceptional advantages in bandwidth, parallelism, and energy efficiency, existing methods optimized for scalar operations struggle to efficiently handle tensor-based tasks, limiting their applicability in complex applications, such as neural networks. Here, we report Parallel Optical Matrix Matrix Multiplication (POMMM), a novel paradigm that enables fully parallel tensor processing through a single coherent light propagation. This approach addresses key limitations of current optical methods, scaling the performance with data dimension, while improving theoretical computational power and efficiency. We demonstrate its high consistency with GPU based matrix matrix multiplication across both real-valued and…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Blind Source Separation Techniques
