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In Cloud Computing platforms the addition of hardware monitoring devices to gather power usage data can be impractical or uneconomical due to the large number of machines to be metered. CloudMonitor, a monitoring tool that can generate…
Active Inference (AIF) is emerging as a powerful framework for decision-making under uncertainty, yet its potential in engineering applications remains largely unexplored. In this work, we propose a novel dual-layer AIF architecture that…
Recent advances in computational materials science present novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds and metastable structures, electronic structure, surface, and…
The limited spectrum resources and dramatic growth of high data rate communications have motivated opportunistic spectrum access using the promising concept of cognitive radio networks. Although this concept has emerged primarily to enhance…
As the Data Science field continues to mature, and we collect more data, the demand to store and analyze them will continue to increase. This increase in data availability and demand for analytics will put a strain on data centers and…
Wireless energy sharing is a novel convenient alternative to charge IoT devices. In this demo paper, we present a peer-to-peer wireless energy sharing platform. The platform enables users to exchange energy wirelessly with nearby IoT…
Understanding the energy consumption pattern in the built environment is invaluable for the evaluation of the sources of energy wastage and the development of strategies for efficient energy management. An integrated monitoring system that…
Currently, data-intensive scientific applications require vast amounts of compute resources to deliver world-leading science. The climate emergency has made it clear that unlimited use of resources (e.g., energy) for scientific discovery is…
Reducing energy consumption is a challenge that is faced on a daily basis by teams from the High-Performance Computing as well as the Embedded domain. This issue is mostly attacked from an hardware perspective, by devising architectures…
The rapid advancement of Artificial Intelligence (AI) has created unprecedented demands for computational power, yet methods for evaluating the performance, efficiency, and environmental impact of deployed models remain fragmented. Current…
Profiling and minimizing the energy consumption of resource-constrained devices is an essential step towards employing IoT in various application domains. Due to the large size and high cost of commercial energy measurement platforms,…
This paper presents a methodology for allocating energy consumption to multiple users of shared data center machines, infrastructure, and software. Google uses this methodology to provide carbon reporting data for enterprise customers of…
Zero-energy devices (ZEDs) are key enablers of sustainable Internet of Things networks by operating solely on harvested ambient energy. Their limited and dynamic energy budget calls for protocols that are energy-aware and intelligently…
The Information and Communication Technology sector accounted for approximately 1.4% of global greenhouse gas emissions and 4% of the world's electricity consumption in 2020, with both expected to rise. To reduce this environmental impact,…
The design of embedded systems is a complex activity that involves a lot of decisions. With high performance demands of present day usage scenarios and software, they often involve energy hungry state-of-the-art computing units. While…
This paper introduces a comprehensive framework aimed at advancing research and policy development in the realm of decarbonization within electric power systems. The framework focuses on three key aspects: carbon accounting, carbon-aware…
The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of…
High-performance computing (HPC) and supercomputing are critical in Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for training complex AI models, which can take from days to months,…
Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement exists. This is of particular importance in scenarios of high dynamics such as energy harvesting or adaptive task scheduling.…
For current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of…