With the continuous advancement of science and technology, technologies in the field of artificial intelligence, such as machine learning (ML) and deep neural networks (DNNs), have gradually become indispensable and important tools in the field of science and engineering. These technologies are not only playing a role in the traditional field of computer science, but also in optics and design. VPIPHOTONICS recently launched the VPItoolKit ML Framework plug-in library, which is designed to support advanced machine learning and deep neural network applications for the design and optimization of optical systems and devices.
The VPICopyToolkit ML Framework plug-in library can be integrated with a variety of tools from the VPIPHOTONICS DesignSuite suite. The plug-in provides the implementation and design of deep neural networks (DNNs) covering multiple application areas, including equalization of optical systems, nonlinear compensation, characterization of optical devices, evaluation, and reverse design. This powerful plugin enables users to easily deploy custom machine learning (ML) algorithms and provides a Python-based, open-source deep neural network (DNN) that users can use right away. Equipped with an intuitive and easy-to-use interface, it is easy for users to manipulate model parameters and convergence constraints.
The purpose of the VPItoolkit ML Framework is to help users build models based on existing evidence and in the presence of uncertainty by collecting known training datasets. These datasets can be used to train DNN models or other supervised custom models. Its flexible data extractor and model loader enable users to manipulate digital, electronic, and optical signals easily and seamlessly. The plug-in is designed with a variety of signal types in mind to meet the data processing needs of different fields. With an intuitive and easy-to-use interface, users can easily access the hyperparameters of deep neural networks (DNNs) to quickly optimize models for better performance. At the same time, the plug-in supports the use of the open-source file format (HDF5) to store large, complex, and heterogeneous data, further enhancing its flexibility and applicability. Here are two typical examples and results:
Deep neural network (DNN)-based in short-range applicationsNRZ and PAM4 signal equalization
Figure 1is the system schematic. On the left, there are modules such as the NRZ transmitter, Mach-Zehnder modulator, etc., and the signal is passed through the fiber to the right receiver and converted to analog to digital to produce an output. The VPItoolkit ML Framework works by inserting a digital data extractor with the input as an ideal bit stream and the output as a skewed signal generated after passing through the system, and the input-output pair is loaded into the deep neural network model algorithm, and the user can set the hyperparameters of the deep neural network (DNN) in the software, including the number of layers in the DNN model, the number of neurons, and the number of cycles for convergence. A large number of training datasets are fed into algorithms to define models for signal equalization, and system outputs are adjusted via DNNs to approximate the ideal input signal.
Figure 2** results are shown, the original sequence is represented in blue, and the output bitstream after DNN equalization is red. The output is highly close to the original signal, which verifies the effectiveness of the model. The same approach applies to coherent systems.
Figure 1
Figure 2
This example easily inserts digital data extractors and model loaders into digital signal processing (DSP) to enable effective integration of DNNs with modern benchmark DSP algorithms for end-to-end performance**. This feature gives users a comprehensive view of the equalization effect and can optimize it if needed. It is worth noting that the model is not limited by a specific signal format and is transparent to any modulation format (M QAM, M PAM, OFDM, etc.) with wide applicability. To help you better understand and apply this example, VPIPHOTONICS provides detailed NRZ and PAM4 demos that guide you step-by-step through how to set up your schematic and use cases.
DNN is used in specific amplifier designs
Characterization of characteristics and performance parameters
Figure 3To qualify a schematic for a Level 2 EDFA with 4 inputs, four signals are multiplexed as inputs, and the signals pass through a fiber amplifier to collect inputs and outputs to train a deep neural network model.
Figure 4By comparing the EDFA model (green line) and the DNN model (blue line), it can be seen that, unlike the physical model, the DNN model enables different configurations to be explored in a simulated environment without risking damage to the physical device in the lab.
Figure 3
Figure 4
The VPItoolkit ML Framework plug-in library has many more application scenarios, such as fiber nonlinearity compensation, optimizing system parameters, and estimating transmission quality, which greatly facilitates the collection and storage of large data sets, provides easy access to DNN hyperparameters without coding, and integrates seamlessly into existing VPIPHOTONICS Design Suite.
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LingyunLight sharesBased on optical technology innovation, we focus on machine vision and fiber optics, and are committed to becoming a global leader in the field of visual artificial intelligence and optoelectronic information. The company has won one first prize of National Technological Invention Award and two second prizes of National Science and Technology Progress Award.
The company's strategy focuses on the machine vision business, adheres to the principle of "implanting eyes and brains for machines", and provides customers with high-end products and solutions such as configurable vision systems, intelligent vision equipment and core vision devices.
In the field of fiber optics,Rooted in the five main application fields of optical fiber technology (fiber laser, fiber sensing, telecommunication communication, data communication, scientific communication), mining excellent product resources such as high-end optoelectronic devices, high-end equipment and instruments with international leading technology, combined with independent research and development products, we hope to build leading high-end product solutions, lead and create domestic industry customer needs, help improve the scientific and technological level in the field of optical technology in China, help major national scientific and technological projects, and help the development of industrialization.