Autofl: Enabling heterogeneity-aware energy efficient federated learning
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while kee** all the raw training …
collaboratively train a shared machine learning model, while kee** all the raw training …
Autoscale: Energy efficiency optimization for stochastic edge inference using reinforcement learning
Deep learning inference is increasingly run at the edge. As the programming and system
stack support becomes mature, it enables acceleration opportunities in a mobile system …
stack support becomes mature, it enables acceleration opportunities in a mobile system …
Smart at what cost? characterising mobile deep neural networks in the wild
With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is
gaining traction as devices become more powerful. With applications ranging from visual …
gaining traction as devices become more powerful. With applications ranging from visual …
A survey on recent OS-level energy management techniques for mobile processing units
To improve mobile experience of users, recent mobile devices have adopted powerful
processing units (CPUs and GPUs). Unfortunately, the processing units often consume a …
processing units (CPUs and GPUs). Unfortunately, the processing units often consume a …
Energy predictive models of computing: theory, practical implications and experimental analysis on multicore processors
The energy efficiency in ICT is becoming a grand technological challenge and is now a first-
class design constraint in all computing settings. Energy predictive modelling based on …
class design constraint in all computing settings. Energy predictive modelling based on …
Contention-aware fair scheduling for asymmetric single-ISA multicore systems
A Garcia-Garcia, JC Saez… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Asymmetric single-ISA multicore processors (AMPs), which integrate high-performance big
cores and low-power small cores, were shown to deliver higher performance per watt than …
cores and low-power small cores, were shown to deliver higher performance per watt than …
Improved multi-core real-time task scheduling of reconfigurable systems with energy constraints
This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous
multicore platforms. Each processor is composed of a set of multi-speed cores with limited …
multicore platforms. Each processor is composed of a set of multi-speed cores with limited …
Energy-aware offloading based on priority in mobile cloud computing
Smartphones and portable devices have been widely used in our daily life. However, these
portable devices cannot be used in a lot of environments due to limitations in battery …
portable devices cannot be used in a lot of environments due to limitations in battery …
Predictive thermal management for energy-efficient execution of concurrent applications on heterogeneous multicores
EW Wächter, C De Bellefroid… - … Transactions on Very …, 2019 - ieeexplore.ieee.org
Current multicore platforms contain different types of cores, organized in clusters (eg, ARM's
big. LITTLE). These platforms deal with concurrently executing applications, having varying …
big. LITTLE). These platforms deal with concurrently executing applications, having varying …
Fedgpo: Heterogeneity-aware global parameter optimization for efficient federated learning
Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in
machine learning training. This approach allows a variety of mobile devices to …
machine learning training. This approach allows a variety of mobile devices to …