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ref: -2017 tags: neuromorphic optical computing nanophotonics date: 06-17-2019 14:46 gmt revision:5 [4] [3] [2] [1] [0] [head]

Progress in neuromorphic photonics

  • Similar idea as what I had -- use lasers as the optical nonlinearity.
    • They add to this the idea of WDM and 'MRR' (micro-ring resonator) weight bank -- they don't talk about the ability to change the weihts, just specify them with some precision.
  • Definitely makes the case that III-V semiconductor integrated photonic systems have the capability, in MMACs/mm^2/pj, to exceed silicon.

See also :

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ref: -0 tags: nanophotonics interferometry neural network mach zehnder interferometer optics date: 06-13-2019 21:55 gmt revision:3 [2] [1] [0] [head]

Deep Learning with Coherent Nanophotonic Circuits

  • Used a series of Mach-Zehnder interferometers with thermoelectric phase-shift elements to realize the unitary component of individual layer weight-matrix computation.
    • Weight matrix was decomposed via SVD into UV*, which formed the unitary matrix (4x4, Special unitary 4 group, SU(4)), as well as Σ\Sigma diagonal matrix via amplitude modulators. See figure above / original paper.
    • Note that interfereometric matrix multiplication can (theoretically) be zero energy with an optical system (modulo loss).
      • In practice, you need to run the phase-moduator heaters.
  • Nonlinearity was implemented electronically after the photodetector (e.g. they had only one photonic circuit; to get multiple layers, fed activations repeatedly through it. This was a demonstration!)
  • Fed network FFT'd / banded recordings of consonants through the network to get near-simulated vowel recognition.
    • Claim that noise was from imperfect phase setting in the MZI + lower resolution photodiode read-out.
  • They note that the network can more easily (??) be trained via the finite difference algorithm (e.g. test out an incremental change per weight / parameter) since running the network forward is so (relatively) low-energy and fast.
    • Well, that's not totally true -- you need to update multiple weights at once in a large / deep network to descend any high-dimensional valleys.