相关论文: SysScale: Exploiting Multi-domain Dynamic Voltage …

Large-scale mobile edge computing (MEC) systems require scalable solutions to allocate communication and computing resources to the users. In this letter we address this challenge by applying dynamic spectrum sharing among the base stations…

The energy footprint of global data movement has surpassed 100 terawatt hours, costing more than 20 billion US dollars to the world economy. Depending on the number of switches, routers, and hubs between the source and destination nodes,…

To attain the targeted data rates of next generation cellular networks requires dense deployment of small cells in addition to macro cells which provide wide coverage. Dynamic radio resource management is crucial to the success of such…

The continuous growth of big data applications with high computational and scalability demands has resulted in increasing popularity of cloud computing. Optimizing the performance and power consumption of cloud resources is therefore…

We demonstrate that general-purpose memory allocation involving many threads on many cores can be done with high performance, multicore scalability, and low memory consumption. For this purpose, we have designed and implemented scalloc, a…

Parallel applications often rely on work stealing schedulers in combination with fine-grained tasking to achieve high performance and scalability. However, reducing the total energy consumption in the context of work stealing runtimes is…

Smartphones, laptops, and data centers are CMOS-based technologies that ushered our world into the information age of the 21st century. Despite their advantages for scalable computing, their implementations come with surprisingly large…

In recent years, the issue of energy consumption in high performance computing (HPC) systems has attracted a great deal of attention. In response to this, many energy-aware algorithms have been developed in different layers of HPC systems,…

Modern multicore processors are employing large last-level caches, for example Intel's E7-8800 processor uses 24MB L3 cache. Further, with each CMOS technology generation, leakage energy has been dramatically increasing and hence, leakage…

Mobile platforms must satisfy the contradictory requirements of fast response time and minimum energy consumption as a function of dynamically changing applications. To address this need, system-on-chips (SoC) that are at the heart of these…

In last decades, dynamic resource programming in partial resource domains has been extensively investigated for single time slot optimizations. However, with the emerging real-time media applications in fifth-generation communications,…

Autonomous mobile agents require low-power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks while adapting to diverse environments, as mobile agents are usually powered by batteries. These requirements can…

Stacked intelligent metasurfaces (SIMs) provide wave-domain degrees of freedom that can empower integrated sensing and communication (ISAC) through flexible beampattern synthesis and interference management, while reducing hardware cost. In…

The recently introduced energy-saving extension of the sub-optimal sliding mode control (SOSMC), which is known in the literature for the last two and half decades, incorporates a control-off mode that allows for saving energy during the…

The enormous technological potential accumulated over the past two decades would make it possible to change the operating principles of power systems entirely. The consequent technological evolution is not only affecting the structure of…

An effective way to improve energy efficiency is to throttle hardware resources to meet a certain performance target, specified as a QoS constraint, associated with all applications running on a multicore system. Prior art has proposed…

Minimizing energy consumption of low-power wireless nodes is a persistent challenge from the constrained Internet of Things (IoT). In this paper, we start from the observation that constrained IoT devices have largely different hardware…

Systolic arrays and shared-L1-memory manycore clusters are commonly used architectural paradigms that offer different trade-offs to accelerate parallel workloads. While the first excel with regular dataflow at the cost of rigid…

Modern computing paradigms, such as cloud computing, are increasingly adopting GPUs to boost their computing capabilities primarily due to the heterogeneous nature of AI/ML/deep learning workloads. However, the energy consumption of GPUs is…

What is the optimal base station (BS) resource allocation strategy given a measurement-based power consumption model and a fixed target user rate? Rush-to-sleep in time, rush-to-mute in space, awake-but-whisper in power, or a combination of…