Related papers: Energy-aware Web Browsing on Heterogeneous Mobile …
As the energy footprint generated by software is increasing at an alarming rate, understanding how to develop energy-efficient applications has become a necessity. Previous work has introduced catalogs of coding practices, also known as…
Multi-page websites dominate modern web development. However, existing design-to-code methods rely on simplified assumptions, limiting to single-page, self-contained webpages without external resource connection. To address this gap, we…
Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enhance the computational capability of wireless devices (WDs).…
Sensing systems powered by energy harvesting have traditionally been designed to tolerate long periods without energy. As the Internet of Things (IoT) evolves towards a more transient and opportunistic execution paradigm, reducing energy…
By year 2020, the number of smartphone users globally will reach 3 Billion and the mobile data traffic (cellular + WiFi) will exceed PC Internet traffic the first time. As the number of smartphone users and the amount of data transferred…
We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We…
Power consumption is a critical consideration in high performance computing systems and it is becoming the limiting factor to build and operate Petascale and Exascale systems. When studying the power consumption of existing systems running…
Heterogeneous computing integrates diverse processing elements, such as CPUs, GPUs, and FPGAs, within a single system, aiming to leverage the strengths of each architecture to optimize performance and energy consumption. In this context,…
Energy use is a key concern when deploying deep learning models on mobile and embedded platforms. Current studies develop energy predictive models based on application-level features to provide researchers a way to estimate the energy…
Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They…
The increasing usage of smartphones in everyday tasks has been motivated many studies on energy consumption characterization aiming to improve smartphone devices' effectiveness and increase user usage time. In this scenario, it is essential…
Mobile Edge Computing (MEC) enables rich services in close proximity to the end users to provide high quality of experience (QoE) and contributes to energy conservation compared with local computing, but results in increased communication…
Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave…
Efficient task partitioning plays a crucial role in achieving high performance at multiprocessor plat forms. This paper addresses the problem of energy-aware static partitioning of periodic real-time tasks on heterogeneous multiprocessor…
In this work we consider battery powered portable systems which either have Field Programmable Gate Arrays (FPGA) or voltage and frequency scalable processors as their main processing element. An application is modeled in the form of a…
Task-based programming models are emerging as a promising alternative to make the most of multi-/many-core systems. These programming models rely on runtime systems, and their goal is to improve application performance by properly…
We propose an energy-efficient controller to minimize the energy consumption of a mobile robot by dynamically manipulating the mechanical and computational actuators of the robot. The mobile robot performs real-time vision-based…
Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to high-fidelity dynamic operation. However, model mismatch…
Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scenario. In this paper, computation offloading from multi-MD to…
Nowadays, the use of soft computational techniques in power systems under the umbrella of machine learning is increasing with good reception. In this paper, we first present a deep learning approach to find the optimal configuration for…