39 citations · 51 across the 13 of their papers we have counts for
13 papers
QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
Xiangchen Li, Saeid Ghafouri, Bo Ji +3
As machine learning inferences increasingly move to edge devices, adapting to diverse computational capabilities, hardware, and memory constraints becomes more critical. Instead of…
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Xiangchen Li, Dimitrios Spatharakis, Saeid Ghafouri +5
The growing gap between the increasing complexity of large language models (LLMs) and the limited computational budgets of edge devices poses a key challenge for efficient on-devic…
HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models
Kazi Hasan Ibn Arif, JinYi Yoon, Dimitrios S. Nikolopoulos +3
High-resolution Vision-Language Models (VLMs) are widely used in multimodal tasks to enhance accuracy by preserving detailed image information. However, these models often generate…
MS-BioGraphs: Sequence Similarity Graph Datasets
Mohsen Koohi Esfahani, Paolo Boldi, Hans Vandierendonck +2
Progress in High-Performance Computing in general, and High-Performance Graph Processing in particular, is highly dependent on the availability of publicly-accessible, relevant, an…
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
JunKyu Lee, Blesson Varghese, Hans Vandierendonck
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy v…
Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques
JunKyu Lee, Lev Mukhanov, Amir Sabbagh Molahosseini +6
Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep ne…
Leveraging Transprecision Computing for Machine Vision Applications at the Edge
Umar Ibrahim Minhas, Lev Mukhanov, Georgios Karakonstantis +2
Machine vision tasks present challenges for resource constrained edge devices, particularly as they execute multiple tasks with variable workloads. A robust approach that can dynam…
TOD: Transprecise Object Detection to Maximise Real-Time Accuracy on the Edge
JunKyu Lee, Blesson Varghese, Roger Woods +1
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 resu…
Understood in Translation, Transformers for Domain Understanding
Dimitrios Christofidellis, Matteo Manica, Leonidas Georgopoulos +1
Knowledge acquisition is the essential first step of any Knowledge Graph (KG) application. This knowledge can be extracted from a given corpus (KG generation process) or specified…
Workload-Aware DRAM Error Prediction using Machine Learning
Lev Mukhanov, Konstantinos Tovletoglou, Hans Vandierendonck +2
The aggressive scaling of technology may have helped to meet the growing demand for higher memory capacity and density, but has also made DRAM cells more prone to errors. Such a re…
VEBO: A Vertex- and Edge-Balanced Ordering Heuristic to Load Balance Parallel Graph Processing
Jiawen Sun, Hans Vandierendonck, Dimitrios S. Nikolopoulos
Graph partitioning drives graph processing in distributed, disk-based and NUMA-aware systems. A commonly used partitioning goal is to balance the number of edges per partition in c…
Energy Optimization of Memory Intensive Parallel workloads
Chhaya Trehan, Hans Vandierendonck, Georgios Karakonstantis +1
Energy consumption is an important concern in modern multicore processors. The energy consumed during the execution of an application can be minimized by tuning the hardware state…
A Programming Model and Runtime System for Significance-Aware Energy-Efficient Computing
Vassilis Vassiliadis, Konstantinos Parasyris, Charalambos Chalios +5
Reducing energy consumption is one of the key challenges in computing technology. One factor that contributes to high energy consumption is that all parts of the program are consid…