Lynsyn and LynsynLite: The STHEM Power Measurement Units

Djupdal, A.; Gottschall, Björn; Ghasemi, Fatemeh; Jahre, Magnus

doi:10.1007/978-3-030-53532-2_6

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…We use the Nordic Semiconductor's nRF52832 SoC [5] as our experimental platform and measure the instantaneous power consumption of the SoC at a frequency of 10 kHz using Lynsyn [6]. The SoC contains an ARM Cortex M4 processor running at 64 MHz and an integrated Bluetooth Low Energy (BLE) radio subsystem.…”

Section: Methodsmentioning

confidence: 99%

Modeling Periodic Energy-Harvesting Computing Systems

Ghasemi

Jahre

2021

IEEE Comput. Arch. Lett.

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The Internet of Things (IoT) requires Ultra-Low Power (ULP) systems that communicate wirelessly and solely rely on harvested energy to scalably interact with the environment. This is challenging for IoT developers because (i) energy and performance are fundamentally intertwined -since capturing sensor samples and communicating more frequently increases energy consumption -and (ii) the performance versus energy trade-off typically needs to evaluated before ULP-platform selection -as the developer needs to be sure that a sufficiently performant system can be built before incurring the (substantial) effort of implementing the application on the ULP-platform. In this paper, we present the Periodic Energy-Harvesting Systems (PES) model which enables such trade-offs by faithfully modeling the energy impact of changing sampling and communication rates. Across our IoT applications, PES has an average energy prediction error of only 0.5%. In contrast, the average error of the state-of-the-art EH-model is 77.0%.

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Section: Methodsmentioning

confidence: 99%

Modeling Periodic Energy-Harvesting Computing Systems

Ghasemi

Jahre

2021

IEEE Comput. Arch. Lett.

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“…The SLL is equipped with a Microcontroller Unit (MCU) [13] and power consumption sensors that are used to operate the PMU-DRL framework. The SLL [14] captures voltage and current information of the NJTX2 at a rate of 2 kilo samples per second (kS/s) and sends it to the PMU-DRL framework for analysis. The framework, based on a deep reinforcement learning algorithm, processes the power information and provides feedback for optimization actions.…”

Section: A Pmu-drl Frameworkmentioning

confidence: 99%

“…In addition, when the board connects to the host workstation via Joint Test Action Group (JTAG). The power measurements can be read directly from the SLL Program Counter (PC) [14]. It can be configured to sample the target signals with frequencies up to 10kHz, and can be used as a generic remotely as an amperimeter or voltmeter.…”

Section: B Power Measurement Unitmentioning

confidence: 99%

Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning

Machado

Zahid

et al. 2020

Electronics

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This paper suggests an optimisation approach in heterogeneous computing systems to balance energy power consumption and efficiency. The work proposes a power measurement utility for a reinforcement learning (PMU-RL) algorithm to dynamically adjust the resource utilisation of heterogeneous platforms in order to minimise power consumption. A reinforcement learning (RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with a Xilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. In this study, the balance operation mode for improving power consumption and performance is established to dynamically change the programmable logic (PL) end work state. It is based on an RL algorithm that can quickly discover the optimization effect of PL on different workloads to improve energy efficiency. The results demonstrate a substantial reduction of 18% in energy consumption without affecting the application’s performance. Thus, the proposed PMU-RL technique has the potential to be considered for other heterogeneous computing platforms.

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