Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

Damsgaard, Hans Jakob; Ometov, Aleksandr; Nurmi, Jari

doi:10.1145/3572772

Cited by 17 publications

(10 citation statements)

References 191 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the author's knowledge, the derivation of offloaded GNSS processing into an Edge scenario has not been reviewed yet. Edge computing has also been reviewed in the scope of Approximate Computing (AxC) techniques [92], which could also prove applicable in the offloaded GNSS processing case.…”

Section: Offloaded or Onboard Processing? (Q4)mentioning

confidence: 99%

A Survey on Low-Power GNSS

Grenier

Lohan

Ometov

et al. 2023

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

With the miniaturization of electronics, Global Navigation Satellite Systems (GNSS) receivers are getting more and more embedded into devices with harsh energy constraints. This process has led to new signal processing challenges due to the limited processing power on battery-operated devices and to challenging wireless environments, such as deep urban canyons, tunnels and bridges, forest canopies, increased jamming and spoofing. The latter is typically tackled via new GNSS constellations and modernization of the GNSS signals. However, the increase in signal complexity leads to higher computation requirements to recover the signals; thus, the trade-off between precision and energy should be evaluated for each application. This paper dives into low-power GNSS, focusing on the energy consumption of satellite-based positioning receivers used in battery-operated consumer devices and Internet of Things (IoT) sensors. We briefly overview the GNSS basics and the differences between legacy and modernized signals. Factors dominating the energy consumption of GNSS receivers are then reviewed, with special attention given to the complexity of the processing algorithms. Onboard and offloaded (Cloud/Edge) processing strategies are explored and compared. Finally, we highlight the current challenges of today's research in low-power GNSS.

show abstract

Section: Offloaded or Onboard Processing? (Q4)mentioning

confidence: 99%

A Survey on Low-Power GNSS

Grenier

Lohan

Ometov

et al. 2023

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…We have chosen the PYNQ environment, developed by Xilinx to enable Python and FPGA interactions on their Zynq-based development boards. Advantages of going from software to hardware are multiple: 1) enabling AxC only accessible at the hardware level [9]; 2) more realistic energy consumption estimates; and 3) processing acceleration. A partial conversion should provide the best of both worlds, that is, efficient processing for computationally intensive tasks, and benchmarking tasks performed in a rich and high-level environment.…”

Section: B Previous Work and Contributionsmentioning

confidence: 99%

“…We have confirmed such computational load during our tests with SyDR.Given the amount of computations, AxC techniques could open a new door to low-power GNSS processing, providing energy consumption reductions. Although most AxC techniques can be applied or emulated in software, many of them can only be applied efficiently in hardware [9]. Moreover, as these techniques often imply consequent accuracy/precision reduction in computations, their effect on the signal tracking and positioning needs to be evaluated.…”

Section: B Sydr An Open-source Benchmark Platformmentioning

confidence: 99%

“…It is crucial to note that AxC can be applied only in applications that are resilient to such errors, i.e., those whose outputs remain acceptable despite some reduced precision. Examples are in Machine Learning (ML) and in DSP algorithms [9]. We note a lack of work using AxC in GNSS processing and highlight it as a promising direction for future research.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Towards Benchmarking GNSS Algorithms on FPGA using SyDR

Grenier

Damsgaard

Liu

et al. 2023

2023 International Conference on Localization and GNSS (ICL-GNSS)

Self Cite

View full text Add to dashboard Cite

Global Navigation Satellite System (GNSS) is widely used today for both positioning and timing purposes. Many distinct receiver chips are available off-the-shelf, each tailored to match various applications' requirements. Being implemented as Application-Specific Integrated Circuits, these chips provide good performance and low energy consumption but must be treated as "black boxes" by customers. This prevents modification, research in GNSS processing chain enhancement (e.g., application of Approximate Computing techniques), and designspace exploration for finding the optimal receiver implementation per each use case. In this paper, we review the development of SyDR, an open-source Software-Defined Radio oriented towards benchmarking of GNSS algorithms. Specifically, our goal is to integrate certain components of the GNSS processing chain in a Field-Programmable Gate Array and manage their operation with a Python program using the Xilinx PYNQ flow. We present the early steps of converting parts of SyDR to C, which will be later converted to Hardware Description Language descriptions using High-Level Synthesis. We demonstrate successful conversion of the tracking process and discuss benefits and drawbacks arising thereof, before outlining next steps in preparation for hardware implementation.

show abstract

“…By moving computational capabilities to the network's edge, edge computing enables faster decision-making, optimized resource utilization, and improved user experiences. It plays a critical role in supporting emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), enabling intelligent edge devices and applications to seamlessly operate across multiple domains, including manufacturing, healthcare, life sciences, and more [13].…”

Section: A Edge Computingmentioning

confidence: 99%

Advancing Medical Device Manufacturing: The Convergence of Edge Computing and Industry 5.0

kumar Dave

2023

IJEAST

View full text Add to dashboard Cite

The integration of edge computing and Industry 5.0 has the potential to revolutionize medical device manufacturing, enhancing the effectiveness, functionality, and safety of medical devices. This research article explores the opportunities and challenges associated with this convergence in the medical device sector. The paper highlights how edge computing enables real-time data processing and analysis at the production site, leading to enhanced real-time monitoring, diagnostics, and predictive maintenance for medical devices. Additionally, Industry 5.0 principles promote human-robot collaboration, personalized healthcare, and intelligent decision-making through data analytics and AI. The paper also addresses the challenges of cybersecurity, privacy concerns, scalability, network infrastructure, and interoperability issues in the implementation of edge computing and Industry 5.0. The article emphasizes the importance of standardized communication protocols and data formats to ensure seamless interoperability. Lastly, it outlines potential areas of research and development, such as realtime analytics, cybersecurity, and human-robot collaboration, to further enhance the integration of edge computing and Industry 5.0 in medical device manufacturing. Researchers, manufacturers, and policymakers stand to benefit from a comprehensive understanding of the opportunities and challenges, enabling the development of strategies to drive positive industry-wide outcomes in medical device manufacturing.

show abstract

Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

Cited by 17 publications

References 191 publications

A Survey on Low-Power GNSS

A Survey on Low-Power GNSS

Towards Benchmarking GNSS Algorithms on FPGA using SyDR

Advancing Medical Device Manufacturing: The Convergence of Edge Computing and Industry 5.0

Contact Info

Product

Resources

About