Announcing VPIphotonics VPItoolkit ML Framework Version 2.0

Date Announced: 28 Aug 2024

Machine learning models, training refinement, and data analysis.

Berlin, Germany – VPItoolkit ML Framework is now available for immediate upgrade.

The VPItoolkit ML Framework is a versatile addition to the simulation tools within the VPIphotonics Design Suite, enabling the implementation and design of deep neural networks (DNNs), recurrent neural networks (RNNs), and machine learning (ML) clustering techniques for a wide range of applications. These include equalization and nonlinearity mitigation in optical transmission systems, characterization, evaluation, optimization, and inverse design of photonic devices, as well as monitoring and optimization of network performance.

The VPItoolkit ML Framework offers a powerful set of capabilities that empower users to deploy custom-made ML algorithms easily. In addition to this flexibility, the framework provides a ready-to-use, open-source, Python-based ML solution with an intuitive interface. This allows users to manipulate model parameters and convergence constraints with ease. By providing an easy-to-use interface to access and adjust the hyperparameters of the ML algorithms, users can quickly optimize and enhance the performance of the models.

VPItoolkit ML Framework Version 2.0 expands on the current software while allowing users to gain deeper insights into ML models, optimize training processes, and tackle a wider range of complex data analysis challenges:

• TensorBoard Visualization: The TensorBoard monitors and analyzes the training process of machine learning models, gaining valuable insights into performance metrics and other relevant information.
• Analyze Data File: The new Analyze Data File macro can inspect the contents of saved training data files, which is used to configure the Model Loader module for visualization.
• Training Parameter: The LearningRate parameter has been added to define the learning rate used in the chosen optimization algorithm and fine-tune learning rates for optimal model performance.
• Recurrent Neural Network (RNN): The RNN model is a neural network designed for processing sequential data. It is implemented as a stack of bidirectional RNN layers, can handle time-domain signals and predict single output values from multiple inputs.
• Machine Learning Clustering: The module Clustering performs input data clustering using the popular K-means algorithm and groups similar data points into clusters.
• Application Demonstrations: New application examples showcase the use of RNNs, K-means clustering, and Jupyter Notebook integration, providing users with a broader set of machine learning and data analysis capabilities.

About VPIphotonics

VPIphotonics sets the industry standard for end-to-end photonic design automation comprising design, analysis and optimization of devices, components, systems and networks. We provide professional simulation software supporting applications in optoelectronics, integrated photonics, fiber optics, optical transmission systems and networks. Our experts offer professional consulting services and training courses on modeling techniques and software capabilities.

For more than 25 years, VPIphotonics' award-winning solutions have been used extensively in research and development and by product design and marketing teams at hundreds of corporations worldwide. Over 160 academic institutions joined our University Program, enabling students, educators, and researchers easy access to VPIphotonics' latest modeling and design innovations.

Contact

EMEA & APAC
VPIphotonics GmbH
Hallerstr. 6
10587 Berlin
Germany
Tel: +49-30-398058-0
Fax: +49-30-398058-58
 
Americas
VPIphotonics Inc.
250 E. Main St., Suite 3700
Rochester, NY 14604
United States
Tel: +1-585-683-8117

E-mail: chris.maloney@vpiphotonics.com

Web Site: www.vpiphotonics.com/index.php