VPItoolkit™ ML Framework - Overview

VPItoolkit ML Framework is a versatile add-on to any of the simulation tools of VPIphotonics Design Suite, enabling the implementation and design of deep neural networks (DNN) for various applications, such as equalization and nonlinearity mitigation for optical systems, device characterization, evaluation and inverse design of photonic devices.

This powerful framework allows the user to easily deploy custom-made machine learning (ML) algorithms. Additionally, it provides a ready-to-use open-source Python-based DNN with an intuitive easy-to-use interface to manipulate model parameters and convergence constraints.

The aim of VPItoolkit ML Framework is to allow the user to build a model that makes predictions based on evidence in the presence of uncertainty, by collecting known training data sets, which are used to train the DNN model or other supervised custom-made models.

Seamless manipulation of digital, electrical and optical signals is feasible, using the framework's flexible Data Extractors and Model Loaders. With a user-friendly access to the DNN hyperparameters, fast optimization can be performed for model performance enhancement.

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VPItoolkit ML Framework supports the storage of large, complex, heterogeneous data in an open-source file format (Hierarchical Data Format version 5, HDF5).

VPItoolkit ML Framework


  • Enhanced DSP equalization

  • Fiber nonlinearity compensation

  • Design and characterization of optical filters

  • Component requirements definition

  • Optimization of system parameters

  • Optical performance monitoring

  • Polarization tracking or phase recovery in quantum communications

  • Quality of transmission estimation


  • Easy collection of training data sets

  • Seamless manipulation of digital, electrical & optical signals

  • On-the-fly hybrid DSP / ML simulations

  • Easy access of DNN hyperparameters

  • Storage of large-size, complex data