1,842 research outputs found
Quantum-noise-limited optical neural networks operating at a few quanta per activation
Analog physical neural networks, which hold promise for improved energy
efficiency and speed compared to digital electronic neural networks, are
nevertheless typically operated in a relatively high-power regime so that the
signal-to-noise ratio (SNR) is large (>10). What happens if an analog system is
instead operated in an ultra-low-power regime, in which the behavior of the
system becomes highly stochastic and the noise is no longer a small
perturbation on the signal? In this paper, we study this question in the
setting of optical neural networks operated in the limit where some layers use
only a single photon to cause a neuron activation. Neuron activations in this
limit are dominated by quantum noise from the fundamentally probabilistic
nature of single-photon detection of weak optical signals. We show that it is
possible to train stochastic optical neural networks to perform deterministic
image-classification tasks with high accuracy in spite of the extremely high
noise (SNR ~ 1) by using a training procedure that directly models the
stochastic behavior of photodetection. 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. Our experiment used >40x fewer photons per inference
than previous state-of-the-art low-optical-energy demonstrations, to achieve
the same accuracy of >90%. Our work shows that some extremely stochastic analog
systems, including those operating in the limit where quantum noise dominates,
can nevertheless be used as layers in neural networks that deterministically
perform classification tasks with high accuracy if they are appropriately
trained.Comment: 55 pages, 27 figure
Roadmap on digital holography [Invited]
This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography
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