Related papers: Accurate Energy Modelling on the Cortex-M0 Process…
The web has become a ubiquitous application development platform for mobile systems. Yet, web access on mobile devices remains an energy-hungry activity. Prior work in the field mainly focuses on the initial page loading stage, but fails to…
Undoubtedly, the increase of available data and competitive machine learning algorithms has boosted the popularity of data-driven modeling in energy systems. Applications are forecasts for renewable energy generation and energy consumption.…
Current approaches to designing energy-efficient applications typically rely on measuring individual components using readily available local metrics, like CPU utilization. However, these metrics fall short when applied to cloud-native…
Managing energy and thermal profiles is critical for many-core HPC processors with hundreds of application-class processing elements (PEs). Advanced model predictive control (MPC) delivers state-of-the-art performance but requires solving…
This work evaluates State-of-the-Art convolution algorithms for CPU-based CNN inference. Although most prior studies focus on GPUs or NPUs, CPU implementations remain comparatively under-optimized. Our first contribution is to provide fair…
Energy consumption of software applications has emerged as a critical concern for developers to contemplate in their daily development processes. Previous studies have surveyed a limited number of developers to understand their viewpoints…
The increasing deployment of large language models (LLMs) in natural language processing (NLP) tasks raises concerns about energy efficiency and sustainability. While prior research has largely focused on energy consumption during model…
The power that machine learning models consume when making predictions can be affected by a model's architecture. This paper presents various estimates of power consumption for a range of different activation functions, a core factor in…
Bluetooth Low Energy (BLE) is a wireless protocol well suited for ultra-low-power sensors running on small batteries. BLE is described as a new protocol in the official Bluetooth 4.0 specification. To design energy-efficient devices, the…
Resource-limited robots face significant challenges in executing computationally intensive tasks, such as locomotion and manipulation, particularly for real-time optimal control algorithms like Model Predictive Control (MPC). This paper…
The Internet of Things (IoT) sparks a whole new world of embedded applications. Most of these applications are based on deeply embedded systems that have to operate on limited or unreliable sources of energy, such as batteries or energy…
Recent breakthrough technological progressions of powerful mobile computing resources such as low-cost mobile GPUs along with cutting-edge, open-source software architectures have enabled high-performance deep learning on mobile platforms.…
We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that…
Complex applications implemented as Systems on Chip (SoCs) demand extensive use of system level modeling and validation. Their implementation gathers a large number of complex IP cores and advanced interconnection schemes, such as…
As AI workloads drive increases in datacenter power consumption, accurate GPU power estimation is critical for proactive power management. However, existing power models face a scalability bottleneck not in the modeling techniques…
Energy systems models, critical for power sector decision support, incur non-linear memory and runtime penalties when scaling up under typical formulations. Even hardware improvements cannot make large models tractable, requiring omission…
Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users. In this paper, we propose a joint resource allocation…
The Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data…
Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory…
Power dissipation and energy consumption have become one of the most important problems in the design of processors today. This is especially true in power-constrained environments, such as embedded and mobile computing. While lowering the…