VCSEL-based Coherent Scalable dEep Learning (VCSEL)

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.

Researchers

Dirk R Englund / Zaijun Chen / Ryan Hamerly

Departments: Dept of Electrical Engineering & Computer Science, Research Laboratory of Electronics
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Networking & Signals
Impact Areas: Advanced Materials

  • vcsel-based coherent scalable deep learning
    Japan | Pending
  • vcsel-based coherent scalable deep learning
    United States of America | Published application

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