Challenges and Future Perspectives of Low-Power VLSI Circuits: A Study

Chauhan, Paramjeet; Gupta, Saptarshi

doi:10.1007/978-981-19-6383-4_46

Cited by 3 publications

(1 citation statement)

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“…The challenge of seamlessly integrating user activities into the resource management framework is difficult, and as low-power embedded systems expand in a variety of applications, scaling problems emerge. Furthermore, the environment is made more complex by the absence of defined protocols and procedures, which requires coordinated efforts to create a unified framework for resource optimization and prescriptive maintenance [10]. Overcoming these obstacles is critical to improving the scalability, efficiency, and dependability of low-power embedded systems.…”

Section: Introductionmentioning

confidence: 99%

LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems

Papaioannou,

Dimara,

Kouzinopoulos

et al. 2024

Sensors

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Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

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Section: Introductionmentioning

confidence: 99%