Efficient FPGA implementations for gradient boosted decision trees with applications in real-time medical imaging systems

This system integrates advanced image enhancement methods with lightweight machine-learning models. It includes optimised techniques for contrast enhancement, convolution operations, and edge detection, establishing the foundation for real-time, efficient image processing. The second contribution focuses on optimising gradient boosted decision tree models for deployment on field-programmable gate arrays.

By introducing training methods that consider the impact of reduced precision during model development, this work reduces the computational and memory demands of these models while maintaining high accuracy. These optimised models significantly minimise resource usage and increase processing speeds, making them well-suited for use in energy-constrained, real-time devices. The final contribution is to develop an adaptive hardware design for gradient boosted decision tree models.

This system allows for dynamic updates to model parameters without the need for reconfiguring the hardware, enabling it to adapt seamlessly to changing data and requirements. This adaptability is critical for medical applications, where evolving data necessitates continuous model updates while maintaining high performance and reliability. Together, these contributions advance the field of embedded artificial intelligence by providing practical strategies for deploying intelligent systems in resource-limited environments.

This work makes a major contribution to the future development of low-power, high-performance medical imaging systems and adaptive artificial intelligence solutions.<br><br><i>Thesis is embargoed until 31 July 2027.</i>