/engineering-onns

This repository contains code, data, and documentation related to the development and evaluation of optical neural network architectures. Our aim is to advance the state of the art in energy-efficient and high-performance computing systems by exploring the use of optical components, such as modulators, waveguides, and resonators.

Primary LanguagePython

Simulating an Electro-Optical Oscillator with Delayed Feedback in Python and TensorFlow

Overview

This instruction provides step-by-step guidance on simulating an electro-optical oscillator with delayed feedback using an FIR filter in Python, and integrating the simulation with TensorFlow. The emphasis is on approximating an ideal analog delay.

Prerequisites

Make sure you have the following prerequisites installed:

  • Python >= 3.8
  • Anaconda (optional)
  • numpy
  • scipy
  • matplotlib
  • tensorflow

You can install the packages using the following command:

::

$ pip install numpy scipy matplotlib tensorflow

Or, if you are using Anaconda:

::

$ conda install numpy scipy matplotlib tensorflow

Define the System

Define the electro-optical oscillator with delayed feedback system in terms of its equations of motion and the parameters that govern its behavior. You can use the equations provided in the paper "Dynamics of Electrooptic Bistable Devices with Delayed Feedback" by Andreas Neyer and Edgar Voges as a starting point.

Implement the FIR Filter

Create an FIR filter with the desired delay characteristics. In this case, you want to approximate an ideal analog delay, which means that the impulse response of the FIR filter should be a scaled version of the ideal impulse response of an infinite analog delay. To calculate the coefficients of the FIR filter, you can use the following steps:

  1. Choose the length of the filter: Determine the length of the FIR filter based on the desired delay and the sampling frequency of the system. A longer filter will give you a better approximation of the ideal analog delay, but will also increase the computational complexity of the simulation.

  2. Calculate the ideal impulse response: Calculate the impulse response of an infinite analog delay with the desired delay time. This can be done analytically using the Laplace transform or numerically using a high-order differential equation solver.

  3. Truncate and scale the impulse response: Truncate the infinite impulse response to the length of the FIR filter and scale it so that the sum of the filter coefficients is equal to one. This will ensure that the FIR filter is a unity-gain filter.

Simulate the System

Implement the electro-optical oscillator with delayed feedback system using the FIR filter as the delay element. Use numerical integration methods, such as the Runge-Kutta method, to solve the system of equations over time. Plot the output of the system to visualize its behavior.

Integrate with TensorFlow

Once you have verified that your simulation is working correctly, you can integrate it with TensorFlow to perform machine learning tasks, such as classification or regression. Use the TensorFlow API to define the neural network architecture and train it using the simulated data.