1,721,018 research outputs found
Dataset for: Efficient State Retention in Transiently Powered Computing Systems
No full textModelled and experimental data obtained throughout the duration of the PhD on "Efficient State Retention in Transiently Powered Computing Systems". Experimental data mainly obtained from two experimental boards: MSP430FR5739 and LPC-Xpresso 810. Modelled data are an output of ModelSim and Design Compiler.</span
Dataset supporting the publication "Energy-efficient memory tracing for state retention in transient computing systems".
No full textThis dataset supports the publication: "Energy-efficient memory tracing for state retention in transient computing systems" by Theodoros D. Verykios, Domenico Balsamo, Geoff V. Merrett, CONFERENCE: 9th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI 2023).
This dataset contains:
- Fig 5, Filename: 'iwasi_dataset.xlsx'
Data: Behaviour of MeTra when running FFT128, with RAM block size of 4 bytes, f_source of 2Hz and Flash as NVM.
-Fig 6 Filename: 'iwasi_dataset.xlsx'
Data: Total active time per EH cycle with varying fsource from 2Hz to 20Hz, using FRAM (top) and Flash (bottom).
-Fig 7 Filename: 'iwasi_dataset.xlsx'
Data: Esnap while performing FFT128, AES128 and CRC32 with f_source = 20Hz (worst case) and using FRAM as NVM.
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Dataset supporting the Extended Abstract titled: "Exploring Energy Efficient State Retention in Transiently-Powered Computing Systems"
No full textExperimental data obtained from two experimental boards: MSP430FR5739 and LPC-Xpresso 810. Data used in extended abstract titled "Exploring Energy Efficient State Retention in Transiently-Powered Computing Systems", accepted for publication in Transiently Powered Computing - IDEA League Doctoral School.</span
Dataset supporting the Paper titled: "Selective Policies for Efficient State Retention in Transiently-Powered Embedded Systems: Exploiting Properties of NVM Technologies"
No full textExperimental data obtained from two experimental boards: MSP430FR5739 and LPC-Xpresso 810. Data used in paper titled "Selective Policies for Efficient State Retention in Transiently-Powered Embedded Systems: Exploiting Properties of NVM Technologies", in Elsevier's Journal on Sustainable Computing: Informatics and Systems.</span
Selective policies for efficient state retention in transiently-powered systems
No full textEnergy harvesting offers the potential for embedded systems to operate without batteries. However, harvesting has been traditionally coupled with large energy buffers such as supercapacitors to mitigate the effect of the source variability. An emerging class of transiently-powered sensing systems enable computation to be sustained during intermittent supply, without using any additional energy storage. To deal with the intermittent nature of the input source, the system state (e.g. registers and RAM) is saved to Non-Volatile Memory (NVM) before a power failure, and restored when the power supply recovers. Existing approaches save the entire state of the system upon power failure, but this is energy and time consuming. In this poster, novel selective policies for efficiently retaining state are explored, which exploit properties of different NVM technologies
ARM mbed support for transient computing in energy harvesting IoT systems
No full textEnergy harvesters offer the possibility for embedded IoT computing systems to operate without batteries. However, their output power is usually unpredictable and highly variable. To mitigate the effect of this variability, systems incorporate large energy buffers, increasing their size, mass and cost. The emerging class of transient computing systems differs from this approach, operating directly from the energy harvesting source and minimizing or removing additional energy storage. Existing transient approaches are largely designed for specific applications and architectures. Hence, they suffer from not being broadly applicable across multiple embedded IoT platforms. To address this challenge, transient approaches need to be integrated within a general IoT programming framework such as ARM’s mbed IoT Device Platform. This support is offered through libraries and application programming interfaces(APIs) which enable transient computing to be implemented as a service on top of IoT application protocols.<br/
Dataset supporting the paper entitled “Power neutral performance scaling for energy harvesting MP-SoCs”
No full textThis dataset supports the paper entitled “Power Neutral Performance Scaling for Energy Harvesting MP-SoCs” accepted for publication</span
Energy-efficient memory tracing for state retention in transient computing systems
No full textTransient computing systems, also known as intermittent computing systems, are batteryless systems powered by energy harvesting (EH) sources that do not require large energy storage for system operations. Instead, they rely on retaining their state, i.e. a snapshot, in non-volatile memory (NVM) in the event of a power outage and restoring it when the power recovers. In this paper, we first discuss the limitations of state-of-the-art techniques that attempt to minimize the amount of system state saved to NVM. Therefore, we propose a novel energy-efficient system-level approach for state retention through memory tracing based on a custom hardware module named MeTra that traces changes in the main (volatile) memory between power outages. MeTra allows the voltage threshold that activates the state retention process to be dynamically adjusted according to the energy requirement of each snapshot. Thus, a great proportion of the energy harvested can be spent on useful operations. Experimental results show that the system’s active time can be extended up to 17x for Flash-based systems and 92.2% for FRAM-based systems, compared to saving the entire system state, with an area overhead of as little as 2.48%
Application- and platform-agnostic runtime power management of heterogeneous embedded systems
No full textIncreasing energy efficiency and reliability at runtime is a key challenge of heterogeneous many-core systems. We demonstrate how contributions from the PRiME project integrate to enable application- and platform-agnostic runtime management that respects application performance targets. We consider opportunities to enable runtime management across the system stack and we enable cross-layer interactions to trade-off power and reliability with performance and accuracy. We consider a system as three distinct layers, with abstracted communication between them, which enables the direct comparison of different approaches, without requiring specific application or platform knowledge. Application-agnostic runtime management is demonstrated with a selection of runtime managers from PRiME, including linear regression modelling and predictive thermal management, operating across multiple applications. Platform-independent runtime management is demonstrated using two heterogeneous platforms
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