This project focuses on optimizing neural networks using techniques such as knowledge distillation, quantization, pruning, and parameter-efficient fine-tuning. The goal of this project is to explore methods that can help reduce the computational complexity and memory footprint of neural networks without compromising their accuracy.

Neural networks have revolutionized the field of machine learning and have become an essential tool for a wide range of applications. However, the ever-increasing complexity of these models can lead to high computational and memory requirements, which can make them impractical to use in certain settings. To address this issue, researchers have developed a variety of techniques to optimize neural networks.

Here are some of the key techniques that we will be exploring in this project:

• Knowledge distillation: This technique involves training a smaller neural network to mimic the behavior of a larger, more complex model. By doing so, we can reduce the computational requirements of deploying neural networks on resource-constrained devices.

• Quantization: This technique involves reducing the precision of weights and activations in a neural network. By using fewer bits to represent these values, we can reduce the memory footprint of the model and potentially speed up its execution.

• Pruning: This technique involves removing unimportant weights and connections from a neural network. By pruning away these unnecessary elements, we can reduce the size of the model and speed up its execution.

• Efficient training of foundation models: These models serve as the basis for many neural network architectures, and by training them efficiently, we can improve the performance of a wide range of tasks.

By exploring these techniques, we aim to provide a set of tools and approaches that can help researchers and practitioners optimize their neural networks for various applications. The techniques we explore in this project are widely used in the field of deep learning and have been shown to be effective in reducing the computational and memory requirements of neural networks.

• Efficient Deep Learning: From Theory to Practice
• LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
• The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
• Harnessing the Power of Sparsity for Large GPT AI Models