Related papers: Variability-Aware Design for Energy Efficient Comp…
The power grid is undergoing significant restructuring driven by the adoption of wind/solar power and the incorporation of new flexible technologies that can shift load in space and time (e.g., data centers, battery storage, and modular…
This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses. Design considerations and challenges such as requirements and design…
Time-varying electricity pricing better reflects the varying cost of electricity compared to flat-rate pricing. Variations between peak and off-peak costs are increasing due to weather variation, renewable intermittency, and increasing…
Renewable sources of energy are the future due to the environmental problems caused by non-renewable sources to produce energy. The biggest issue with renewable energy sources is that the power produced by devices such as PV solar panels…
The pursuit of energy transition necessitates the coordination of several technologies, including more efficient and cost-effective distributed energy resources (DERs), smart grids, carbon capture, utilization, and storage (CCUS),…
Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has found wide-spread adoption in accelerating neural networks for…
The densification and expansion of wireless networks pose new challenges on energy efficiency. With a drastic increase of infrastructure nodes (e.g. ultra-dense deployment of small cells), the total energy consumption may easily exceed an…
Accurate and reliable measurement of energy consumption is critical for making well-informed design choices when choosing and training large scale NLP models. In this work, we show that existing software-based energy measurements are not…
Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and…
Energy management, and in particular its optimization, is one of the hot trends in the current days, both at the enterprise level (optimization of whole corporate/government buildings) and single citizens' homes. The current trend is to…
Scientific software applications are increasingly developed by large interdiscplinary teams operating on functional modules organized around a common software framework, which is capable of integrating new functional capabilities without…
The advance of autonomous Smart Sensor Networks and embedded systems for the Internet of Things, powered by photovoltaic energy harvesting, is severely limited by energy efficiency, especially in low-light environments. While Dynamic Power…
In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring. As a result, an increasing number of AI-enabled…
When considering different hardware platforms, not just the time-to-solution can be of importance but also the energy necessary to reach it. This is not only the case with battery powered and mobile devices but also with high-performance…
Performance variability has been acknowledged as a problem for over a decade by cloud practitioners and performance engineers. Yet, our survey of top systems conferences reveals that the research community regularly disregards variability…
As power management has become a primary concern in modern data centers, computing resources are being scaled dynamically to minimize energy consumption. We initiate the study of a variant of the classic online speed scaling problem, in…
Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of multicore CPUs, general-purpose many-core GPUs, and…
Sustainable AI is a subfield of AI for concerning developing and using AI systems in ways of aiming to reduce environmental impact and achieve sustainability. Sustainable AI is increasingly important given that training of and inference…
In literature computer architectures are frequently claimed to be highly flexible, typically implying there exist trade-offs between flexibility and performance or energy efficiency. Processor flexibility, however, is not very sharply…
Nowadays, the use of mobile applications and terminals faces fundamental challenges related to energy constraint. This is due to the limited battery lifetime as compared to the increasing hardware evolution. Video streaming is one of the…