3 research outputs found
Characterizing Accuracy Trade-offs of EEG Applications on Embedded HMPs
Electroencephalography (EEG) recordings are analyzed using battery-powered
wearable devices to monitor brain activities and neurological disorders. These
applications require long and continuous processing to generate feasible
results. However, wearable devices are constrained with limited energy and
computation resources, owing to their small sizes for practical use cases.
Embedded heterogeneous multi-core platforms (HMPs) can provide better
performance within limited energy budgets for EEG applications. Error
resilience of the EEG application pipeline can be exploited further to maximize
the performance and energy gains with HMPs. However, disciplined tuning of
approximation on embedded HMPs requires a thorough exploration of the
accuracy-performance-power trade-off space. In this work, we characterize the
error resilience of three EEG applications, including Epileptic Seizure
Detection, Sleep Stage Classification, and Stress Detection on the real-world
embedded HMP test-bed of the Odroid XU3 platform. We present a combinatorial
evaluation of power-performance-accuracy trade-offs of EEG applications at
different approximation, power, and performance levels to provide insights into
the disciplined tuning of approximation in EEG applications on embedded
platforms.Comment: 7 pages, 10 figure
Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches
Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided
Energy Harvesting and Energy Storage Systems
This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources