Efficient Compute at the Edge: Optimizing Energy Aware Data Structures for Emerging Edge Hardware

Khan, Amin M.; Umar, Ibrahim; Ha, Phuong Hoai

doi:10.1109/hpcs.2018.00060

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…In the last decade, the application of machine learning techniques in data analysis has been revolutionary [88]. Advances in deep learning techniques [89], the present-day possibilities of collecting and accessing large amounts of data, the processing capacity of computing systems (dedicated processors such as GPUs, cards designed for the computation of artificial intelligence algorithms), as well as the design of new architectures [90] (e.g., edge computing designs combined with a vision processing unit (VPU) or a tensor processing unit (TPU) hardware [91]), have all led to breakthroughs in this area. This continuous evolution has been crystallized in the new paradigms of AI related to the learning capacities of machines, such as deep learning solutions [92] and reinforced learning (RL) techniques.…”

Section: Deep Learningmentioning

confidence: 99%

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Márquez-Sánchez,

Calvo-Gallego,

Erbad

et al. 2023

Electronics

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Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment.

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Section: Deep Learningmentioning

confidence: 99%

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Márquez-Sánchez,

Calvo-Gallego,

Erbad

et al. 2023

Electronics

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“…However, some papers [74][75][76][77][78] refer to resource-scarce (or similar nomenclature), but they use devices such as Raspberry Pi (RPi), which cannot be considered as resource-scarce in the scope of this paper, and in many IoT applications. Moreover, an RPi or a BeagleBone can run high-level languages like python, which simplifies the process of training the model in a Cloud computer, and export it to these platforms through the use of tools like Pickle [79].…”

Section: Machine Learning In End-device Embedded Systemsmentioning

confidence: 99%

Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

2019

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The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence.

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“…The aim is to devise a typical embarrassingly parallel workload that is compute-intensive. In the Mpeg benchmark, 7 each task executes an MPEG algorithm.…”

Section: ) Considered Benchmarksmentioning

confidence: 99%

“…Among state-of-the-art compute platforms [7] entering the race to solve the edge computing challenges, we can mention the Intel Movidius Myriad technology [8], the Samsung Exynos 9 Series 9810 processor [9], the Jetson TX2 board [10] and the Machine and Object Detection processors announced by ARM in its Trillium project [11]. An important aim of these platforms is to provide power-efficient compute capabilities for embedded artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

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Towards Energy-Efficient Heterogeneous Multicore Architectures for Edge Computing

et al. 2019

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In recent years, the edge computing paradigm has been attracting much attention in the Internetof-Things domain. It aims to push the frontier of computing applications, data, and services away from the usually centralized cloud servers to the boundary of the network. The benefits of this paradigm shift include better reactivity and reliability, reduced data transfer costs toward the centralized cloud servers, and enhanced confidentiality. The design of energy-efficient edge compute nodes requires, among others, low power cores such as microprocessors. Heterogeneous architectures are key solutions to address the crucial energy-efficiency demand in modern systems. They combine various processors providing attractive power and performance trade-offs. Unfortunately, no standard heterogeneous microcontroller-based architecture exists for edge computing. This paper deals with the aforementioned issue by exploring typical low power architectures for edge computing. Various heterogeneous multicore designs are developed and prototyped on FPGA for unbiased evaluation. These designs rely on cost-effective and inherently ultra-low power cores commercialized by Cortus SA, a world-leading semiconductor IP company in the embedded ultra-low power microcontroller domain. Some microarchitecture-level design considerations, e.g., floating point and out-of-order computing capabilities, are taken into account for exploring candidate solutions. In addition, a tailored and flexible multi-task programming model is defined for the proposed architecture paradigm. We analyze the behavior of various application programs on available core configurations. This provides valuable insights on the best architecture setups that match program characteristics, so as to enable increased energy-efficiency. Our experiments on multi-benchmark programs show that on average 22% energy gain can be achieved (up to 45%) compared to a reference system design, i.e., a system with the same execution architecture, but agnostic of the task management insights gained from the comprehensive evaluation carried out in this work.INDEX TERMS Edge computing, energy-efficiency, heterogeneous multicore architectures, programming model, embedded systems.

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Efficient Compute at the Edge: Optimizing Energy Aware Data Structures for Emerging Edge Hardware

Cited by 7 publications

References 18 publications

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

Towards Energy-Efficient Heterogeneous Multicore Architectures for Edge Computing

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