1 code implementation • 19 Jan 2025 • Shashikant Ilager, Lukas Florian Briem, Ivona Brandic
Results show that our method reduces the energy consumption between 23-50 % on average for code generation tasks without significantly affecting accuracy.
no code implementations • 14 Jan 2025 • Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
To that end, in this work, we investigate the effect of important parameters on the performance and energy efficiency of LLMs during inference and examine their trade-offs.
no code implementations • 31 Oct 2024 • Daniel May, Alessandro Tundo, Shashikant Ilager, Ivona Brandic
While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task.
no code implementations • 14 Oct 2024 • Meerzhan Kanatbekova, Shashikant Ilager, Ivona Brandic
Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled and latency-sensitive services to application users using Machine Learning (ML) algorithms, e. g., Time Series Classification (TSC).
1 code implementation • 13 May 2024 • Shashikant Ilager, Jakob Fahringer, Alessandro Tundo, Ivona Brandić
However, traditional monitoring systems have a centralized architecture for both data plane and control plane, which increases latency, creates a failure bottleneck, and faces challenges in providing quick and trustworthy data in volatile edge environments, especially where infrastructures are often built upon failure-prone, unsophisticated computing and network resources.
no code implementations • 25 Mar 2024 • Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e. g., mobile devices, IoT edge nodes).
no code implementations • 6 Sep 2023 • Daniel Hofstätter, Shashikant Ilager, Ivan Lujic, Ivona Brandic
Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols.
1 code implementation • 31 Aug 2023 • Alessandro Tundo, Marco Mobilio, Shashikant Ilager, Ivona Brandić, Ezio Bartocci, Leonardo Mariani
In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e. g., accuracy in object detection and frames processing rate) with energy consumption.
no code implementations • 6 Jul 2021 • Shashikant Ilager, Rajkumar Buyya
This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency.
no code implementations • 7 Nov 2020 • Shashikant Ilager, Kotagiri Ramamohanarao, Rajkumar Buyya
Specifically, we propose a gradient boosting machine learning model for temperature prediction.
1 code implementation • 1 Sep 2020 • Shreshth Tuli, Shashikant Ilager, Kotagiri Ramamohanarao, Rajkumar Buyya
The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources.
no code implementations • 9 Jun 2020 • Shashikant Ilager, Rajeev Muralidhar, Rajkumar Buyya
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries.