Hexadecimal Neural Networks with IntelHex: A Novel Approach to Machine Learning

Introduction

In the ever-evolving field of machine learning, much of the focus remains on optimizing models through advanced architectures, better datasets, and faster training mechanisms. However, one often overlooked area is how data and model parameters (weights, biases) are represented and processed. Floating-point arithmetic, while standard in most neural networks, may not always be the most efficient representation, especially in memory-constrained or embedded systems where power and processing resources are limited.

In this article, I will explore a novel approach to neural network design — one that leverages hexadecimal data formats and the IntelHex library to store and manipulate model parameters. I will dive deeply into the theoretical underpinnings of this approach, analyse its advantages in specific use cases, and compare it to traditional floating-point neural networks. Along the way, we will dissect the code, explain the underlying mathematical principles, and provide insights into why this method may be advantageous for certain machine learning applications.

By the end of this article, you will have a thorough understanding of how hexadecimal representations can be integrated into neural networks, the trade-offs they introduce, and the…