On-Chip Optical Convolutional Neural Networks
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Summary
A photonics circuit architecture which could consume a fraction of energy per inference compared with state of the art electronics is proposed.
- Type
- preprint
- Published
- 2018-08-09
- Cited by
- 81
- References
- 63
- Access
- Open access
- OpenAlex
- https://openalex.org/W2886970706
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51965960
Keywords
Convolutional neural network, Chip, Computer science, Artificial intelligence, Telecommunications
References
- cuDNN: Efficient Primitives for Deep Learning
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Monolayer graphene as a saturable absorber in a mode-locked laser
- Nonlinear mirror based on two-photon absorption
- Quantum transport simulations in a programmable nanophotonic processor
- A convolutional neural network cascade for face detection
- Perfect optics with imperfect components
- Fast bistable all-optical switch and memory on a silicon photonic crystal on-chip.
- Pulse transmission through a saturable absorber
- 1.1 Computing's energy problem (and what we can do about it)
- Area-efficient differential Gaussian circuit for dedicated hardware implementations of Gaussian function based machine learning algorithms
- Energy-efficient active photonics in a zero-change, state-of-the-art CMOS process
- An analog continuous-time neural network
- Trapping and delaying photons for one nanosecond in an ultrasmall high-Q photonic-crystal nanocavity
- Experimental demonstration of reservoir computing on a silicon photonics chip
- Optical delay lines based on optical filters
- In-Plane Optical Absorption and Free Carrier Absorption in Graphene-on-Silicon Waveguides
- The perceptron: a probabilistic model for information storage and organization in the brain.
- An analog dynamic memory array for neuromorphic hardware
- Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing
Cited by
- Quantum optical neural networks
- Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
- Large-Scale Optical Neural Networks based on Photoelectric Multiplication
- High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays.
- Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)
- A Winograd-Based Integrated Photonics Accelerator for Convolutional Neural Networks
- Ultrafast and energy-efficient all-optical switching with graphene-loaded deep-subwavelength plasmonic waveguides
- Challenges in the Path Toward a Scalable Silicon Photonics Implementation of Deep Neural Networks
- Class-specific differential detection in diffractive optical neural networks improves inference accuracy
- Efficient training and design of photonic neural network through neuroevolution
- High-energy-efficiency integrated photonic convolutional neural networks
- Solving computer vision tasks with diffractive neural networks
- Photonic Neural Networks: A Survey
- Silicon Photonics Codesign for Deep Learning
- Femtojoule per MAC Neuromorphic Photonics: An Energy and Technology Roadmap
- Photonic Convolutional Neural Networks Using Integrated Diffractive Optics
- Inverse design of an integrated-nanophotonics optical neural network.
- All-Optical WDM Recurrent Neural Networks With Gating
- Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks
- Toward Hardware-Efficient Optical Neural Networks: Beyond FFT Architecture via Joint Learnability
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