US 12,511,527 B2 · VCSEL-based coherent scalable deep learning

Abstract

The exponential growth in deep learning models is challenging existing computing hardware. Optical neural networks (ONNs) accelerate machine learning tasks with potentially ultrahigh bandwidth and nearly no loss in data movement. Scaling up ONNs involves improving scalability, energy efficiency, compute density, and inline nonlinearity. However, realizing all these criteria remains an unsolved challenge. Here, we demonstrate a three-dimensional spatial time-multiplexed ONN architecture based on dense arrays of microscale vertical cavity surface emitting lasers (VCSELs). The VCSELs, coherently injection-locked to a leader laser, operate at gigahertz data rates with a 7T-phase-shift voltage on the 10-millivolt level. Optical nonlinearity is incorporated into the ONN with no added energy cost using coherent detection of optical interference between VCSELs.

Claims17

Dependent claims

An optical neural network comprising:

an array of vertical-cavity surface-emitting lasers (VCSELs) comprising a first VCSEL to emit a first beam phase-modulated with an activation vector and second VCSELs to emit second beams phase-modulated with weights of a weight matrix of the optical neural network and coherent with the first beam;
a diffractive optical element, in optical communication with the first VCSEL, to fan out the first beam;
and
an array of photodetectors, in optical communication with the array of VCSELs and the diffractive optical element, to detect interference between respective fanned-out copies of the first beam from the diffractive optical element and the second beams.

1 more element

Dependencies are inferred from the claim text.

VCSEL-based coherent scalable deep learning

Life of this patent

VCSEL-based coherent scalable deep learning

Life of this patent

Abstract

Claims17