Integrated photonic 3D tensor processing engine
Yue Wu, Ziheng Ni, Xin Li, Yuanxun Wang, Liangjun Lu, Jianping Chen, Linjie Zhou

TL;DR
This paper introduces a photonic processor that efficiently handles 3D tensor operations for deep learning, achieving high accuracy in LiDAR image recognition.
Contribution
The novel 3D tensor processing engine integrates optical caching, synchronization, and computation for high-order tensor convolutions.
Findings
The 3D-TPE operates at clock frequencies from 10 GHz to 30 GHz.
It achieves 97.06% accuracy in LiDAR 3D point cloud classification.
Optical components reduce memory and time overheads compared to electrical reshaping.
Abstract
Optical computing leverages high bandwidth, low latency, and power efficiency, which is considered as one of the most effective solutions for accelerating deep learning tasks. However, mainstream photonic hardware accelerators are primarily optimized for two-dimensional (2D) matrix-vector multiplications (MVMs). To implement three-dimensional (3D) convolutional neural networks (CNNs), high-order tensors must be reshaped in the electrical domain according to the size of the accelerators before computation, leading to extra memory usage and time overheads. Additionally, synchronization across multiple channels depends on external electronic clocks, which increases the complexity of the system. In this work, we propose an integrated photonic 3D tensor processing engine (3D-TPE) based on the interleaving modulation of time, wavelength, and space. Data caching, channel synchronization and…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Photonic and Optical Devices · Optical Network Technologies
