Online Energy-Efficient Task-Graph Scheduling for Multicore Platforms

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Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

Various interactions, time arrangements, and constraints exist for individuals scheduling their day as a member of a household, which affect their in-home as well as out-of-home activity schedule. However, the existing activity-based models are mostly base ...

This paper focuses on 5G RAN slicing, where the 5G radio resources must be divided across slices (or enterprises) so as to achieve high spectrum efficiency, fairness and isolation across slices, and the ability for each slice to customize how the radio res ...

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

Various forms of real-world data, such as social, financial, and biological networks, can be represented using graphs. An efficient method of analysing this type of data is to extract subgraph patterns, such as cliques, cycles, and motifs, from graphs. For ...

Energy efficiency is crucial for the sustainable operation of all industrial and urban sectors. However, practicing engineers have seldom access to open-source tools that can readily evaluate and compare scenarios in terms of energy consumption, cost, and ...

The reduction of energy consumption in the residential building stock and cement industry is a substantial component of the Swiss Energy Strategy 2050. Therefore, it is vital to identify the potential for EEI in these two sectors. In this study, a new meth ...

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in r ...

A central task in high-level synthesis is scheduling: the allocation of operations to clock cycles. The classic approach to scheduling is static, in which each operation is mapped to a clock cycle at compile-time, but recent years have seen the emergence o ...

Dampers are devices that reduce delay jitter in the context of time-sensitive networks, by delaying packets for the amount written in packet headers. Jitter reduction is required by some real-time applications; beyond this, dampers have the potential to so ...

High-level synthesis (HLS) tools typically generate statically scheduled datapaths. Static scheduling implies that the resulting circuits have a hard time exploiting parallelism in code with potential memory dependences, with control dependences, or where ...

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even ...

Remote health monitoring has attracted a lot of attention over the past decades to provide the opportunity for early detection of pathological health conditions. This early detection improves the quality of life for the patients and significantly reduces t ...

Electric vehicles (EVs) are already part of today's reality and their number is expected to grow rapidly in the near future. A large-scale penetration of EVs will increase the power consumption during charging periods. Hence, the uncoordinated and random c ...

In recent years, population aging and the consequent higher incidence of noncommunicable diseases, have increased the need for long-term health monitoring. Moreover, as healthcare cost is projected to grow substantially by 2030 in OECD countries, the deman ...

Online Energy-Efficient Task-Graph Scheduling for Multicore Platforms

Chattez avec Graph Search

OASIS: An integrated optimisation framework for activity scheduling

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Simulating intra-household interactions for in- and out-of-home activity scheduling

Channel-Aware 5G RAN Slicing with Customizable Schedulers

Chaosity: Understanding Contemporary NUMA-architectures

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems

Acceleration of graph pattern mining and applications to financial crime

ROSMOSE: A Web-based Optimization Tool to Aid Decision-making for the Design and Operation of Industrial and Urban Energy Systems

Endogenous Energy Efficiency Improvement in Housing and Cement Sectors in Switzerland

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

DASS: Combining Dynamic & Static Scheduling in High-Level Synthesis

Analysis of Dampers in Time-Sensitive Networks With Non-Ideal Clocks

From C/C plus plus Code to High-Performance Dataflow Circuits

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

Random Surface Covariance Estimation by Shifted Partial Tracing

Self-Aware Machine Learning for Chronic Pathology Monitoring on Wearable Devices

Grid-Aware Real-Time Control of Electric Vehicles

System-level Design of Adaptive Wearable Sensors for Health and Wellness Monitoring