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Hyper-Parallel Optical Tensor Processors Toward Multitask Neural Networks
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DOI：10.1002/lpor.71796.png)
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Abstract
En 中文
The exponential growth of multimodal sensor data demands computing architectures capable of massively parallel tensor operations with ultralow latency and high energy efficiency. Optical computing offers a promising route to overcome the von Neumann bottleneck and inherent limits on parallelism in electronic systems. Here, we report a hyper-parallel distributed multi-core photonic processor (DMPP) that orchestrates the intrinsic orthogonality of light across spectral and spatial domains to enable multiple-instruction, multiple-data processing. Our approach integrates wavelength-division multiplexing (WDM) with spatially distributed photonic tensor cores, allowing a single processor to process multiple independent data streams and computational tasks concurrently. The system supports multitask workflows by dynamically sharing photonic cores across tasks. This work establishes a scalable photonic computing paradigm for high-throughput processing of heterogeneous sensor data, enabling efficient, parallel neural network accelerators for edge intelligence and multimodal analytics.
Keywords:
multimodal
multitask
optical neural network
parallel optical computing
Journal
L
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10
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Citations:
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