We are seeking to recruit a an excellent PhD candidate on the project “SWEET: Hardware and Software for Sustainable Wearable Edge InTelligence”, seeking to optimise performance and energy efficiency of machine learning inference in response to time-varying conditions. Interested applicants can apply here: https://www.qub.ac.uk/courses/postgraduate-research/phd-opportunities/optimising-speed-energy-and-quality-of-machine-learning-models-with-transprecise-computing.html
Hans Vandierendonck
36 posts
The SWEET project will investigate the efficient deployment and sustainability issues of wearable sensors in particular for health analytics. Real-time remote monitoring of physiological indicators can support early detection and intervention in heart diseases and save lives. These services however require wearable technologies with strong predictive abilities, fast networks and […]
We have 3 recruitment opportunities in a Marie Curie Doctoral Training Network on data analytics. These are PhD opportunities with a research assistant contract: (1) Application-Aware Relaxed Synchronisation for Distributed Graph Processing, (offered)(2) Interactive and intelligent exploration of big complex data, (offered) and(3) Efficient and Responsible Analytics for Urban Mobility […]
This is a follow-up work on TOD, which selects one of multiple deep neural networks (DNNs) to perform real-time video analytics (object detection) on low-end devices, e.g., on the camera itself. TOD uses the median object size to determine which one out of 4 YOLO DNNs will meet the real-time […]
Real-time video analytics on the edge is challenging as the computationally constrained resources typically cannot analyse video streams at full fidelity and frame rate, which results in loss of accuracy. We propose a Transprecise Object Detector (TOD) which maximises the real-time object detection accuracy on an edge device by selecting […]
DOI: 10.1145/3524059.3532360 This paper proposes software-defined floating-point number formats for graph processing workloads, which can improve performance in irregular workloads by reducing cache misses. Efficient arithmetic on software-defined number formats is challenging, even when based on conversion to wider, hardware-supported formats. We derive efficient conversion schemes that are tuned to […]
Finally got around to this: publishing the Graptor source code. With time passing, the code has changed quite a bit compared to that used in the paper: Graptor: efficient pull and push style vectorized graph processing. The evolution of the code has advantages: it’s faster. There are also disadvantages: not […]
17 June 2021 Abstract: Large graphs are behind many problems in today’s computing landscape. Thegrowing sizes of such graphs, reaching 70 trillion edges recently, requireunprecedented amounts of compute power, storage, and energy. In this talk, weillustrate how to effectively process such extreme-scale graphs. We will firstdiscuss Slim Graph, the first […]
3 June 2021 Abstract: The emergence of big data in recent years due to the vast societal digitalization and large-scale sensor deployment has entailed significant interest in machine learning methods to enable automatic data analytics. In a majority of the learning algorithms used in industrial as well as academic settings, the first-order iterative […]
Abstract Graph algorithms are widely used in several application domains. It has been established that parallelizing graph algorithms is challenging. The parallelization issues get exacerbated when graphics processing unit (GPU) is used to execute graph algorithms. In particular, three important GPU-specific aspects affect performance: memory coalescing, memory latency, and thread […]