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.
VPItoolkit ML Framework supports the storage of large, complex, heterogeneous data in an open-source file format (Hierarchical Data Format version 5, HDF5).
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
Seamless integration into
and related add-on toolkits