Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices

This repository contains the code associated with the following lab sessions:

• Lab 1: Training Machine Learning Models
• Lab 2: Applying Model Compression Techniques

To run the code, you will need to set up a Python environment as described below.

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1. Install Miniconda (Linux)

Step 1: Download the Installer

Open a terminal and run:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

Step 2: Run the Installer

bash Miniconda3-latest-Linux-x86_64.sh

Follow the prompts:

• Press Enter to scroll through the license agreement.
• Type yes to accept the license.
• Choose an installation location (the default is usually fine).
• When asked to initialize Conda, type yes.

Step 3: Activate Conda

Close and reopen your terminal, or run:

source ~/.bashrc

Verify the installation:

conda --version

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2. Create the Environment from environment.yml

Make sure the environment.yml file is in your current directory, then run:

conda env create -f environment.yml

This command will:

• Read the dependencies from the file.
• Install all required packages automatically.
• Create the Conda environment with the name specified in the file.

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3. Activate the Environment

conda activate neuralEnv10

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