Andres Gomez

Mixed-criticality runtime mechanisms and evaluation on multicores

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 systems 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 EDFVD [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.

Dynamic energy burst scaling for transiently powered systems

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 sources 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 loads 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 harvesters 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 3.89mW.

Self-powered wireless sensor nodes for monitoring radioactivity in contaminated areas using unmanned aerial vehicles

A self-sustainable wireless sensor node for the monitoring radiation in contaminated and poorly accessible areas is presented. The node is designed to work in collaboration with an unmanned aerial vehicle used for two essential mission steps: air-deploying the wireless sensor nodes at suitable locations and acquiring data logs via ultra-low power, short-range radio communication in fly-by mode, after a wake-up routine. The system allows for the use of off-the-shelf components for defining mission, drop-zone and trajectory, for compressing data, and for communication management. The node is equipped with a low-power nuclear radiation sensor and it was designed and implemented with self-sustainability in mind as it will be deployed in hazardous, inaccessible areas. To this end, the proposed node uses a combination of complementary techniques: a low-power microcontroller with non-volatile memory, energy harvesting, adaptive power management and duty cycling, and a nano-watt wake-up radio. Experimental results show the power consumption efficiency of the solution, which achieves 70uW in sleep mode and 500uW in active mode. Finally, simulations based on actual field measurements confirm the solutions self-sustainability and illustrate the impact of different sampling rates and that of the wake-up radio.

Reducing energy consumption in microcontroller-based platforms with low design margin co-processors

Advanced energy minimization techniques (i.e. DVFS, Thermal Management, etc) and their high-level HW/SW requirements are well established in high-throughput multi-core systems. These techniques would have an intolerable overhead in low-cost, performance-constrained microcontroller units (MCUs). These devices can further reduce power by operating at a lower voltage, at the cost of increased sensitivity to PVT variation and increased design margins. In this paper, we propose an runtime environment for next-generation dual-core MCU platforms. These platforms complement a single-core with a low area overhead, reduced design margin shadow-processor. The runtime decreases the overall energy consumption by exploiting design corner heterogeneity between the two cores, rather than increasing the throughput. This allows the platforms power envelope to be dynamically adjusted to application-specific requirements. Our simulations show that, depending on the ratio of core to platform energy, total energy savings can be up to 20%.

SF3P: a framework to explore and prototype hierarchical compositions of real-time schedulers

The trend to integrate multiple functionalities on the same (off-the-shelf) hardware has made the selection of the right scheduling algorithm and configuration difficult. This selection requires the designer to validate any scheduling decision already during early design steps on the target architecture, e.g., by using a reconfigurable scheduling framework running in the user-space. In this paper, we first identify the requirements that such a scheduling framework must fulfill. Then, we propose SF3P: an open-source framework that meets these requirements. To this end, we define an interface common to all scheduling algorithms and separate the scheduling algorithm from its low-level implementation. With these features, SF3P can not only prototype a scheduler at high level of abstraction, but also execute the implemented task-set on specific hardware. Furthermore, SF3P can hierarchically compose scheduling algorithms, useful in the mixed criticality domain, and could also be used to explore different scheduling policies in the system optimization phase. We demonstrate these features by implementing SF3P on top of a POSIX-compliant operating system on two different platforms: Raspberry Pi and an Intel Core i7 desktop system.

Measurement and validation of energy harvesting IoT devices

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 μ8 guarantees continuous power measurements starting from 4pW at 1 mV up to 2.75 W at 5.5 V. The software provides remote control and manages data acquisition of up to 13Mb/ sec in real-time. We extensively characterize the RocketLoggers 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.

Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network

MEMS sensor technology and advances in electronics, low-power processors and communication have enabled ubiquitous monitoring, providing significant opportunities for a wide range of applications including wearable devices for fitness and health tracking. However, due to the limited form factor required, there remains a challenging issue that limits even more the success of wearable devices: the limited lifetime due to the small energy storages that supply the devices. This limitation affects usability and forces the data processing to keep low-complexity to match the power constraints. As wireless communication is typically the most power hungry activity in wearable sensors devices, many techniques focus on reducing the communication power consumption. For this reason, advanced power management can be exploited to increase the lifetime of the devices. In this work, we present a wireless body area network with an adaptive power management strategy combining an ultra-low power wake up radio with context awareness. The context aware power manager is based on activity recognition, which is evaluated to decide which other nodes must be activated. The nano-power wake up receiver is used to reduce the idle listening power of the main radio and enable an asynchronous ultra-low power protocol. In order to evaluate the benefit, we present a real world application to assist elderly people in gait rehabilitation through a closed loop feedback. Experimental results demonstrate the benefit of the proposed power management in terms of energy efficiency. We evaluate the overall power consumption of the system and the lifetime extension, which can increase up to a factor of 4 depending on the amount of time the system can be placed in sleep mode.

Wearable, energy-opportunistic vision sensing for walking speed estimation

State-of-the-art wearable systems are typically performance-constrained, battery-based devices which can, at most, reach self-sustainability using energy harvesting and aggressive duty-cycling. In this work, we present a wearable vision sensor node which can reliably execute computationally-intensive computer-vision algorithms in an energy-opportunistic fashion. By leveraging a burst-generation scheme, the proposed system can efficiently provide the energy guarantees required for tasks with temporal dependencies, even under highly variable harvesting conditions. By mounting the node on a users glasses, the node is able to acquire a sequence of images and determine the users walking speed, requiring only a small solar panel and capacitor. Both hardware and software have been fully optimized for ultra-low power consumption and high performance. Extensive experimental results show the energy nodes energy proportionality and the accuracy of its walking speed estimation.

Self-Sustainability in Nano Unmanned Aerial Vehicles: A Blimp Case Study

Nowadays nano Unmanned Aerial Vehicles (UAVs), such as quad-copters, have very limited flight times, tens of minutes at most. The main constraints are energy density of the batteries and the engine power required for flight. In this work, we present a nano-sized blimp platform, consisting of a helium balloon and a rotorcraft. Thanks to the lift provided by helium, the blimp requires relatively little energy to remain at a stable altitude. We also introduce the concept of duty-cycling high power actuators, to reduce the energy requirements for hovering even further. With the addition of a solar panel, it is even feasible to sustain tens or hundreds of flight hours in modest lighting conditions (including indoor usage). A functioning 52 gram prototype was thoroughly characterized and its lifetime was measured in different harvesting conditions. Both our system model and the experimental results indicate our proposed platform requires less than 200 mW to hover in a self sustainable fashion. This represents, to the best of our knowledge, the first nano-size UAV for long term hovering with low power requirements.

Efficient, Long-Term Logging of Rich Data Sensors Using Transient Sensor Nodes

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 43.4mW load with efficiencies of up to 79.7% and can work with input power levels as low as 140μW. 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%.