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Hypermultiplexed Integrated-Photonics-based Tensor Optical Processor
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abstract
The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT) and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here we demonstrate a hypermultiplexed integratedphotonics-based tensor optical processor (HITOP) that can perform trillions of operations per second (TOPS) at the energy efficiency of 40 TOPS/W. Space-time-wavelength three-dimensional (3D) optical parallelism enables O($N^{2}$) operations per clock-cycle using O($N$) modulator devices. The system is built with wafer-fabricated III/V micron-scale lasers and high-speed thin-film Lithium-Niobate electro-optics for encoding at 10s femtojoule/symbol. Lasing threshold incorporates analog inline rectifier (ReLu) nonlinearity for low-latency activation. The system scalability is verified with machine learning models of 405,000 parameters. A combination of high clockrates, energy-efficient processing and programmability unlocks the potential of light for large-scale AI accelerators in applications ranging from training of large AI models to real-time decision making in edge deployment.
Forward citations
Cited by 3 Pith papers
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A fully-programmable integrated photonic processor for both domain-specific and general-purpose computing
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Physics-Informed Neural Networks for Programmable Origami Metamaterials with Controlled Deployment
The abstract claims a data-free PINN that programs energy landscapes of conical Kresling origami, but the body text is an unrelated photonics paper, leaving the central claim unevaluable.
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Scalable intensity-based photonic matrix-vector multiplication processor using single-wavelength time-division-multiplexed signals
A 32-channel silicon photonic chip performs time-division-multiplexed, intensity-only matrix-vector multiplication and runs MNIST convolution with 93.47% accuracy.
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