VPItoolkit™ ML Framework

14 Sep 2022

New machine learning framework to design and implement deep neural networks for optical communication systems and photonic devices.

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 provides an open-source Python-based DNN with an intuitive easy-to-use interface to manipulate model parameters and convergence constraints. Additionally, it allows
for easy deployment of custom-made machine learning (ML) algorithms by the user.

The aim of VPItoolkit ML Framework is to build a model that makes predictions based on evidence in the presence of uncertainty, bycollecting known training data sets, which are used to train the
supervised DNN model.

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.

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

Typical Applications