Related papers: BEEBS: Open Benchmarks for Energy Measurements on …
Measuring energy consumption is a challenging task faced by developers when building mobile apps. This paper presents EMaaS: a system that provides reliable energy measurements for mobile applications, without requiring a complex setup. It…
The memory system of a modern embedded processor consumes a large fraction of total system energy. We explore a range of different configuration options and show that a reconfigurable design can make better use of the resources available to…
In recent years, due to a higher demand for portable devices, which provide restricted amounts of processing capacity and battery power, the need for energy and time efficient hard- and software solutions has increased. Preliminary…
This work presents a practical benchmarking framework for optimizing artificial intelligence (AI) models on ARM Cortex processors (M0+, M4, M7), focusing on energy efficiency, accuracy, and resource utilization in embedded systems. Through…
Rapid progress in microelectromechanical system (MEMS) and radio frequency (RF) design has enabled the development of low-power, inexpensive, and network-enabled microsensors. These sensor nodes are capable of capturing various physical…
Operating systems have historically had to manage only a single type of memory device. The imminent availability of heterogeneous memory devices based on emerging memory technologies confronts the classic single memory model and opens a new…
In this paper, we present a bit stream feature based energy model that accurately estimates the energy required to decode a given HEVC-coded bit stream. Therefore, we take a model from literature and extend it by explicitly modeling the…
In this paper we leverage an existing general framework for resource usage verification and specialize it for verifying energy consumption specifications of embedded programs. Such specifications can include both lower and upper bounds on…
We investigate an approach that uses low-level analysis and the execution-cache-memory (ECM) performance model in combination with tuning of hardware parameters to lower energy requirements of memory-bound applications. The ECM model is…
Trusted execution environments (TEEs) such as \intelsgx facilitate the secure execution of an application on untrusted machines. Sadly, such environments suffer from serious limitations and performance overheads in terms of writing back…
Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing…
The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in…
Embedded applications are widely used in portable devices such as wireless phones, personal digital assistants, laptops, etc. High throughput and real time requirements are especially important in such data-intensive tasks. Therefore,…
The scale of scientific High Performance Computing (HPC) and High Throughput Computing (HTC) has increased significantly in recent years, and is becoming sensitive to total energy use and cost. Energy-efficiency has thus become an important…
Energy consumption in current large scale computing infrastructures is becoming a critical issue, especially with the growing demand for centralized systems such as cloud environments. With the advancement of microservice architectures and…
Optimizing scientific applications to take full advan-tage of modern memory subsystems is a continual challenge forapplication and compiler developers. Factors beyond working setsize affect performance. A benchmark framework that…
This paper investigates the application of a robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs. A 64-bit Tegra TX1 SoC is configured with…
Recent proliferation of embedded systems has generated a bold new paradigm, known as open embedded systems. While traditional embedded systems provide only closed base applications (natively-installed software) to users, open embedded…
AI workloads, particularly those driven by deep learning, are introducing novel usage patterns to high-performance computing (HPC) systems that are not comprehensively captured by standard HPC benchmarks. As one of the largest academic…
Computer science marches towards energy-aware practices. This trend impacts not only the design of computer architectures, but also the design of programs. However, developers still lack affordable and accurate technology to measure energy…