On the optimization of imaging pipelines

Schoonhoven, R.A. (2024)

Doctoral Thesis

In this thesis, topics relating to the optimization of high-throughput pipelines used for imaging are discussed. In particular, different levels of implementation, i.e., conceptual, software, and hardware, are discussed and the thesis outlines how advances on each level need to be made to make gains in computationally demanding imaging applications. Chapter 2 concerns the implementation of real-time segmentation of X-ray computed tomography (CT). Chapter 3 concerns the end-to-end optimization of various CT workflows by using auto-differentiation frameworks. Chapter 4 concerns a novel pruning method for neural network to significantly increase the speed of convolutional neural networks (CNNs). Chapter 5 comprises a benchmark study of optimization algorithms for tuning GPU kernels, and introduces a novel graph-based approach to quantify search space difficulty. Chapter 6 introduces a novel model to improve the energy efficiency of GPUs.

• Computed tomography
• Computational imaging
• HPC
• GPU
• Deep learning
• Optimization
• Pruning
• Inverse problems
• Machine learning
• Evolutionary computing

• All authors: Schoonhoven, R.A.
• Supervisor: Batenburg, K.J.
• Co-supervisor: Pelt, D.M.; Werkhoven, B.J.C. van
• Committee: Bäck, T.H.W.; Nieuwpoort, R.V. van; Plaat, A.; Staring, M.; Cao, L.; Viganò, N.
• Qualification: Doctor (dr.)
• Awarding Institution: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
• Date: 2024-06-11