multi-stage optimization algorithm

Efficient Deep Neural Network Inference with Near-Edge Accelerator Offload

Hao Liu, Ph.D. Student, Computer Science

Nov 11, 09:30 - 11:30

B2 R5220

edge computing Efficient AI accelerators multi-stage optimization algorithm distributed computing Distributed algorithms

This thesis develops efficient distributed inference strategies for deep neural networks that exploit near-edge accelerators through edge, near-edge, and cloud tier offloading, optimized model partitioning, and collaborative expert-based processing to achieve low-latency, energy-efficient, and accurate AI inference on resource-constrained edge systems.

Dynamic Programming Multi-Objective Combinatorial Optimization

Michal A. Mankowski, Ph.D., Computer Science

Oct 13, 15:00 - 16:00

KAUST

combinatorial optimization global sequence alignment multi-stage optimization algorithm

In this dissertation, we consider extensions of dynamic programming for combinatorial optimization. We introduce two exact multi-objective optimization algorithms: the multi-stage optimization algorithm that optimizes the problem relative to the ordered sequence of objectives (lexicographic optimization) and the bi-criteria optimization algorithm that simultaneously optimizes the problem relative to two objectives (Pareto optimization).

Hao Liu

Ph.D. Student, Computer Science

edge computing Efficient AI deep neural networks LLM cloud technologies distributed computing Distributed algorithms distributed deep learning multi-stage optimization algorithm

Hao Liu is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), advised by Prof. Suhaib Fahmy, where his research focuses on efficient deep neural network inference, edge and near-edge AI acceleration, distributed computing, and optimization techniques for low-latency, energy-efficient intelligent systems.