The FERNS project is an MSCA Doctoral Network aiming to design eco-friendly electronics and accelerate their uptake. This multi-disciplinary project spans across the fields of material science, engineering, social science and business to acquire a holistic perspective of the field of sustainable electronics. In DIPSA, we will investigate the system […]
Transprecision
12 posts
We are please to announce that Zohreh Moradinia has won the Best Poster Award at SIMULTECH 2025 for her work on “Machine Learning-Driven Framework for Identifying Parameter-Driven Anomalies in Multiphysics Simulations”. This work investigates whether errors in scientific simulations can be detected using machine learning. Zohreh assumes errors resulting from […]
The SOFTNUM project, funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101031148, has introduced a novel paradigm where number formats are software-defined rather than hardwired. This advancement moves beyond traditional computing, which relies on conventional hardware-supported floating-point number formats. In […]
Our group has published a new paper in the IEEE Transactions on Emerging Topics in Computing. In this study, we introduced Software-Defined Floating-Point (SDF) number formats designed to enhance the performance of the Belief Propagation (BP) algorithm, which is widely used in fields like machine learning, communications, and robotics. Traditional […]
We are currently seeking to appoint an exceptional candidate to the post of Research Fellow. The post holder will perform research on deployment of machine-learned models for health analytics on distributed IoT/edge/cloud systems using transprecise computing and contribute to the research project “Sustainable Wearable Edge InTelligence (SWEET)”. The successful candidate […]
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
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 […]
This projects aims to develop algorithms for real-time processing for analytics in digital factories. A particular use case is the design of hardware circuits, where silicon can easily be damaged during manufacturing. The production defects that arise negatively affect yield and/or quality of the devices. Uncovering these defects, and how […]
Computing devices implement computer arithmetic as basic functionality, and they implement the same, standardized number formats in order to support software portability. However, with Moore’s Law ending, we question whether it remains the best approach to achieve high performance and low energy consumption by applying the same standardized number formats […]
Publications The DiPET project investigates models and techniques that enable distributed stream processing applications to seamlessly span and redistribute across fog and edge computing systems. The goal is to utilize devices dispersed through the network that are geographically closer to users to reduce network latency and to increase the available […]