distributed computing

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.

Jianwei Shi

Postdoctoral Research Fellow, Statistics

distributed computing decentralized systems Statistical Modelling geospatial statistics geospatial modeling

Assessing Network Middlebox Impact on End-to-End Protocol Behavior via a Distributed and Reprogrammable Framework

Ilies Benhabbour, Ph.D. Student, Computer Science

Mar 4, 13:00 - 16:00

B5 L5 R5220

cybersecurity Cryptography distributed computing

This dissertation focuses on the detection and verification of network middleboxes thanks to the creation of a new distributed framework called NoPASARAN.

EF-BV: distributed optimization with compressed communication

Laurent Condat, Senior Research Scientist, Computer Science

Oct 10, 12:00 - 13:00

B9 L2 R2322 H1

big data distributed computing Federated learning

In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

Mojtaba Alshams

Ph.D. Student, Electrical and Computer Engineering

distributed computing Deep learning Computer Vision edge compute paradigms distributed deep learning edge devices energy flexible and efficient inference

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.

Ali Charara

Ph.D., Computer Science

High Performance Computing algorithms distributed computing Linux GPU Computing OpenMP parallel computing

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)