All-Photonic Artificial Neural Network Processor Via Non-linear Optics
- URL: http://arxiv.org/abs/2205.08608v1
- Date: Tue, 17 May 2022 19:55:30 GMT
- Title: All-Photonic Artificial Neural Network Processor Via Non-linear Optics
- Authors: Jasvith Raj Basani, Mikkel Heuck, Dirk R. Englund, Stefan Krastanov
- Abstract summary: We propose an all-photonic artificial neural network processor.
Information is encoded in the amplitudes of frequency modes that act as neurons.
Our architecture is unique in providing a completely unitary, reversible mode of computation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optics and photonics has recently captured interest as a platform to
accelerate linear matrix processing, that has been deemed as a bottleneck in
traditional digital electronic architectures. In this paper, we propose an
all-photonic artificial neural network processor wherein information is encoded
in the amplitudes of frequency modes that act as neurons. The weights among
connected layers are encoded in the amplitude of controlled frequency modes
that act as pumps. Interaction among these modes for information processing is
enabled by non-linear optical processes. Both the matrix multiplication and
element-wise activation functions are performed through coherent processes,
enabling the direct representation of negative and complex numbers without the
use of detectors or digital electronics. Via numerical simulations, we show
that our design achieves a performance commensurate with present-day
state-of-the-art computational networks on image-classification benchmarks. Our
architecture is unique in providing a completely unitary, reversible mode of
computation. Additionally, the computational speed increases with the power of
the pumps to arbitrarily high rates, as long as the circuitry can sustain the
higher optical power.
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