Energy harvesting is generally seen to be the key to power cyber-physical systems in a low-cost, long term, efficient manner. However, harvesting has traditionally been coupled with large energy storage devices to mitigate the effects of the source's variability. The emerging class of transiently powered systems avoids this issue by performing computation only as a function of the harvested energy, minimizing the obtrusive and expensive storage element. In this work, we present an efficient Energy Management Unit (EMU) to supply generic loads when the average harvested power is much smaller than required for sustained system operation. By building up charge to a pre-defined energy level, the EMU can generate short energy bursts predictably, even under variable harvesting conditions. Furthermore, we propose a dynamic energy burst scaling (DEBS) technique to adjust these bursts to the load's requirements. Using a simple interface, the load can dynamically configure the EMU to supply small bursts of energy at its optimal power point, independent from the harvester's operating point. Extensive theoretical and experimental data demonstrate the high energy efficiency of our approach, reaching up to 73.6% even when harvesting only 110 µW to supply a load of 3.89 mW.

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With the appearance of wearable devices and the IoT, energy harvesting nodes are becoming more and more important. The design and evaluation of these small standalone sensors and actuators, which harvest limited amounts of energy, requires novel tools and methods. Fast and accurate measurement systems are required to capture the rapidly changing harvesting scenarios and characterize leakage currents and energy efficiencies. The need for real-world experiments creates a demand for compact and portable equipment to perform in-situ power measurements and environmental logging. This work presents the ROCKETLOGGER, a hand-held measurement device that combines both properties: portability and accuracy. The custom analog front-end allows logging at sampling rates up to 64 kSPS. The fast range switching within 1.4 µs guarantees continuous power measurements starting from 4 pW at 1 mV up to 2.75 W at 5.5 V. The software provides remote control and manages data acquisition of up to 13 Mb/ sec in real-time. We extensively characterize the ROCKETLOGGER's performance, demonstrate the need for its properties in three use-cases at different stages of the system design flow, and show its advantages in measuring and validating new harvesting-driven devices for the IoT.

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade’s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually.

Multicore systems are being increasingly used for embedded system deployments, even in safety-critical domains. Co-hosting applications of different criticality levels in the same platform requires sufficient isolation among them, which has given rise to the mixed-criticality scheduling problem and several recently proposed policies. Such policies typically employ runtime mechanisms to monitor task execution, detect exceptional events like task overruns, and react by switching scheduling mode. Implementing such mechanisms efficiently is crucial for any scheduler to detect runtime events and react in a timely manner, without compromising the system's safety. This paper investigates implementation alternatives for these mechanisms and empirically evaluates the effect of their runtime overhead on the schedulability of mixed-criticality applications. Specifically, we implement in user-space two state-of-the-art scheduling policies: the flexible time-triggered FTTS [1] and the partitioned EDF-VD [2], and measure their runtime overheads on a 60-core Intel R Xeon Phi and a 4-core Intel R Core i5 for the first time. Based on extensive executions of synthetic task sets and an industrial avionic application, we show that these overheads cannot be neglected, esp. on massively multicore architectures, where they can incur a schedulability loss up to 97%. Evaluating runtime mechanisms early in the design phase and integrating their overheads into schedulability analysis seem therefore inevitable steps in the design of mixed-criticality systems. The need for verifiably bounded overheads motivates the development of novel timing-predictable architectures and runtime environments specifically targeted for mixed-criticality applications.

While energy harvesting is generally seen to be the key to power cyber-physical systems in a low-cost, long term, efficient manner, it has generally required large energy storage devices to mitigate the effects of the source's variability. The emerging class of transiently powered systems embrace this variability by performing computation in proportion to the energy harvested, thereby minimizing the obtrusive and expensive storage element. By using an efficient Energy Management Unit (EMU), small bursts of energy can be buffered in an optimally-sized capacitor and used to supply generic loads, even when the average harvested power is only a fraction of that required for sustained system operation. Dynamic Energy Burst Scaling (DEBS) can be used by the load to dynamically configure the EMU to supply small bursts of energy at its optimal power point, independent from the harvester's operating point. Parameters like the maximum burst size, the solar panel's area as well as the use of energy-efficient Non-Volatile Memory Hierarchy (NVMH) can have a significant impact on the transient system's characteristics such as the wake-up time and the amount of work that can be done per unit of energy. Experimental data from a solar-powered, long-term autonomous image acquisition application show that, regardless of its configuration, the EMU can supply energy bursts to a 43.4 mW load with efficiencies of up to 79.7% and can work with input power levels as low as 140 µW. When the EMU is configured to use DEBS and NVMH, the total energy cost of acquiring, processing and storing an image can be reduced by 77.8%, at the price of increasing the energy buffer size by 65%. CCS Concepts: •Computer systems organization → Embedded hardware;

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