Dr Vasilios Kelefouras

Since September 2018, I have held the position of a Lecturer (Assistant Professor) at the University of Plymouth within the Department of Computing. My research area, in simple terms, lies in the broad field of optimizing software applications, e.g., Deep Neural Network (DNN) applications, in terms of execution time, energy consumption, and memory footprint; my research includes a diverse spectrum of areas, including low-level hardware-dependent compiler optimizations/ techniques for CPUs, GPUs, and FPGAs, high-level compression-based optimizations for DNNs, such as filter pruning, low-rank factorization and quantization, task scheduling, and memory management strategies. 

In 2013, I received my PhD from the department of Electrical and Computer Engineering at University of Patras; I composed and won the Greek PhD Research Scholarship. 
From September 2013 until December 2016 I had been working as a postdoctoral researcher at VLSI lab at Dept. of Electrical and Computer Engineering, University of Patras. Additionally, from October 2015 until December 2016, I had been working as a postdoctoral researcher at Embedded System Design and Application Lab of Technological Educational Institute of Western Greece. From Jan. 2017 until Dec. 2017 I was a Research Fellow at Distributed Systems and Services Research Group, School of Computing, University of Leeds (UK). Last, from Dec. 2017 until Sept. 2018 I had been working as a Lecturer at Sheffield Hallam University.

Currently, I am the module leader of the following modules

Admin Roles

Research Interests: 

I have strong R&D experience in optimizing software applications, in terms of execution time, energy consumption and memory size, on a wide range of hardware platforms including embedded systems, GPUs and FPGAs. I have published more than 45 research papers in high quality journals and conferences, such as IEEE/ACM transactions. I am currently supervising three PhD students.  

Research Highlights:

Compiler Optimizations for accelerating Deep Neural Networks [1]: Convolution layers are the main performance bottleneck in many classes of Deep Neural Networks and especially in Convolutional Neural Networks which are widely used in artificial intelligence applications such as computer vision. In this research work [1], a novel analytical methodology is developed for super-fast convolution layers on CPUs. The experimental results, which include 112 different convolution layers and two hardware platforms, show that the convolution layers of ResNet-50, DenseNet-121 and SqueezeNet are executed from x1.1 up to x7.2 times faster than Intel oneDNN state of the art library. 

[1] V. Kelefouras and G. Keramidas, “Design and Implementation of Deep Learning 2D Convolutions on modern CPUs,” IEEE Transactions on Parallel and Distributed Systems, 2023 

Compiler Optimizations for accelerating Smoothing, Sharpening and Edge Detection, image/video processing software applications [2]: A novel methodology is developed [2] to speedup Smoothing, Sharpening and Edge Detection algorithms on CPUs. To accelerate such routines, the popular OpenCV library supports Intel IPP optimized library to accelerate its routines on Intel CPUs (not by default - this needs to be specified during the installation phase). Based on our experimental results, which include 20 different image sizes and two hardware platforms, the proposed methodology achieves from x2.8 to x40 speedup over the Intel IPP / OpenCV library for GaussianBlur() and Filter2D() OpenCV routines.  

[2] V. Kelefouras and G. Keramidas, “Design and implementation of 2D convolution on x86/x64 processors,” IEEE Transactions on Parallel and Distributed Systems, 2022 

PhD Students:

PI in ‘Development of GIB optimisation for investigating local scour around complex structure for marine restoration and protection’. This research grant is funded by Engys LTD and University RnD solutions fund (£30,000). Partners are Engys LTD and ARC Marine companies. April 2023 - Dec. 2023.

PI in 'Optimisation of sparse matrix solvers for Computational Fluid Dynamics', funded by RnD solutions fund (£2,000). June 2023 - July 2023.

Program Committee Member