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Found 4 papers, 2 papers with code
Quantum-noise-limited optical neural networks operating at a few quanta per activation
We experimentally demonstrated MNIST classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to 0. 008 photons per multiply-accumulate (MAC) operation, which is equivalent to 0. 003 attojoules of optical energy per MAC.
Optical Transformers
We conclude that with well-engineered, large-scale optical hardware, it may be possible to achieve a $100 \times$ energy-efficiency advantage for running some of the largest current Transformer models, and that if both the models and the optical hardware are scaled to the quadrillion-parameter regime, optical computers could have a $>8, 000\times$ energy-efficiency advantage over state-of-the-art digital-electronic processors that achieve 300 fJ/MAC.
Image sensing with multilayer, nonlinear optical neural networks
In image sensing, a measurement, such as of an object's position, is performed by computational analysis of a digitized image.
An optical neural network using less than 1 photon per multiplication
Here, we experimentally demonstrate an optical neural network achieving 99% accuracy on handwritten-digit classification using ~3. 2 detected photons per weight multiplication and ~90% accuracy using ~0. 64 photons (~$2. 4 \times 10^{-19}$ J of optical energy) per weight multiplication.
