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An Electro-Photonic System for Accelerating Deep Neural Networks
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Photonic-Electronic Integrated Circuits for High-Performance Computing and AI Accelerators
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Noise-Resilient Photonic Analog Neural Networks
inferred← citeshttps://doi.org/10.1109/jlt.2025.3537286openalex-snapshot
Ultra-Efficient Si3N4 MZIs With BaTiO3 as Weight Elements for Neuromorphic Photonics
inferred← citeshttps://doi.org/10.1109/jphot.2023.3338415openalex-snapshot
Waveguide-Integrated Plasmonic Photodetectors and Activation Function Units With Phase Change Materials
inferred← citeshttps://doi.org/10.1109/jstqe.2022.3171167openalex-snapshot
Coherent Photonic Crossbar Arrays for Large-Scale Matrix-Matrix Multiplication
inferred← citeshttps://doi.org/10.1109/mcas.2026.3688606openalex-snapshot
Harnessing Photonics for Machine Intelligence [Feature]
inferred← citeshttps://doi.org/10.1117/12.2633916openalex-snapshot
Photonic tensor core for machine learning: a review
inferred← citeshttps://doi.org/10.1117/12.2647179openalex-snapshot
Photonic tensor core machine learning accelerator
inferred← citeshttps://doi.org/10.1117/12.2650228openalex-snapshot
Design and testing of silicon photonic 4F system for convolutional neural networks
inferred← citeshttps://doi.org/10.1117/12.2666848openalex-snapshot
An energy-efficient quantized inference framework for electro-photonic computing system
inferred← citeshttps://doi.org/10.1117/12.2678666openalex-snapshot
PhotoFourier: silicon photonics joint transfer correlator for convolution neural network
inferred← citeshttps://doi.org/10.1117/12.2690776openalex-snapshot
A wavelength-multiplexed photonic tensor processor based on Mach-Zehnder modulator
inferred← citeshttps://doi.org/10.1134/s0021364024603956openalex-snapshot
Numerical Simulation of a Photonic Tensor Core for the Hardware Acceleration of the Optical Matrix–Vector Multiplication
inferred← citeshttps://doi.org/10.1145/3579371.3589110openalex-snapshot
Flumen: Dynamic Processing in the Photonic Interconnect
inferred← citeshttps://doi.org/10.1145/3695053.3731053openalex-snapshot
LightML: A Photonic Accelerator for Efficient General Purpose Machine Learning
inferred← citeshttps://doi.org/10.1145/3711845openalex-snapshot
HEANA : A Hybrid Time-Amplitude Analog Optical Accelerator with Flexible Dataflows for Energy-Efficient CNN Inference
inferred← citeshttps://doi.org/10.1145/3721145.3734529openalex-snapshot
ROCKET: An RNS-based Photonic Accelerator for High-Precision and Energy-Efficient DNN Training
inferred← citeshttps://doi.org/10.1145/3818624openalex-snapshot
LightSim: A Flexible, Fast Modeling Framework for Holistic Photonic Computing Systems
inferred← citeshttps://doi.org/10.1364/cleo_at.2025.aa113_1openalex-snapshot
A Flexible Testbed for Prototyping Photonic Processors
inferred← citeshttps://doi.org/10.1364/cleo_si.2024.sm4m.6openalex-snapshot
Integrated Coherent Photonic Crossbar Arrays for Efficient Optical Computing
inferred← citeshttps://doi.org/10.1364/cleo_si.2026.stu2f.4openalex-snapshot
Abstracting Away Photonic Devices: Cross-Functional Simulation of Photonic AI Accelerators
inferred← citeshttps://doi.org/10.1364/oe.537813openalex-snapshot
Efficient on-chip training of large-scale optical neural network through block adjoint training algorithm
inferred← citeshttps://doi.org/10.1364/oe.585151openalex-snapshot
Reconfigurable mode converters based on low-loss phase change materials for integrated optical computing
inferred← citeshttps://doi.org/10.1364/oe.604976openalex-snapshot
Optical processing units vs. GPUs and TPUs: conditions for performance and energy advantage
inferred← citeshttps://doi.org/10.1364/ofc.2024.tu3f.5openalex-snapshot
Optical Neural Networks with Tensor Compression and Photonic Memory
inferred← citeshttps://doi.org/10.1364/optica.507525openalex-snapshot
Realization of an integrated coherent photonic platform for scalable matrix operations
inferred← citeshttps://doi.org/10.1364/optica.584297openalex-snapshot
High-speed electro-optic engine for a quantized photonic neural network
inferred← citeshttps://doi.org/10.1515/nanoph-2022-0049openalex-snapshot
Design automation of photonic resonator weights
inferred← citeshttps://doi.org/10.1515/nanoph-2023-0406openalex-snapshot
Multiplexing in photonics as a resource for optical ternary content‐addressable memory functionality
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1513759/v1openalex-snapshot
Delocalized Photonic Deep Learning on the Internet's Edge
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1833027/v1openalex-snapshot
High-density Integrated Photonic Tensor Processing Unit with a Matrix Multiply Compiler
inferred← citeshttps://doi.org/10.23919/date56975.2023.10137248openalex-snapshot
Scalable Coherent Optical Crossbar Architecture using PCM for AI Acceleration
inferred← citeshttps://doi.org/10.23919/date56975.2023.10137317openalex-snapshot
Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics
inferred← citeshttps://doi.org/10.31857/s0370274x24120227openalex-snapshot
Chislennoe modelirovanie fotonnogo tenzornogo yadra dlya apparatnogo uskoreniya opticheskikh matrichno-vektornykh vychisleniy
inferred← citeshttps://doi.org/10.3389/aot.2024.1501208openalex-snapshot
Pruning and optimization of optical neural network as a binary optical trigger
inferred← citeshttps://doi.org/10.3389/fphy.2022.1064693openalex-snapshot
Optoelectronic integrated circuits for analog optical computing: Development and challenge
inferred← citeshttps://doi.org/10.3389/fphy.2024.1369099openalex-snapshot
A review of emerging trends in photonic deep learning accelerators
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