Hexadecimal Quantisation for Big Parameter Models: A Memory-Efficient & Accurate Approach

Introduction

In the field of deep learning, one of the challenges is the size and memory footprint of models, particularly large-scale language models such as GPT. The need for efficiency in memory usage and computational power is becoming increasingly important, especially in environments with limited resources, such as edge devices, mobile platforms, and IoT systems.

This article will introduce a novel approach: Hexadecimal Quantisation. By converting neural network weights from floating-point values into a hexadecimal format, we can significantly reduce memory usage without excessively sacrificing performance. We will also compare this approach to Binary Quantisation, which offers more aggressive memory savings but at a higher cost to model accuracy.

The article will cover:

• The theoretical foundation behind quantisation techniques.
• An in-depth breakdown of the code that implements hexadecimal quantisation.
• Comparisons between hexadecimal and binary quantisation.
• Use cases and benefits of hexadecimal quantisation in various applications.
• A look at how this quantisation compares with traditional…