Domenico Balsamo

Hibernus: Sustaining Computation During Intermittent Supply for Energy-Harvesting Systems

A key challenge to the future of energy-harvesting systems is the discontinuous power supply that is often generated. We propose a new approach, Hibernus, which enables computation to be sustained during intermittent supply. The approach has a low energy and time overhead which is achieved by reactively hibernating: saving system state only once, when power is about to be lost, and then sleeping until the supply recovers. We validate the approach experimentally on a processor with FRAM nonvolatile memory, allowing it to reactively hibernate using only energy stored in its decoupling capacitance. When compared to a recently proposed technique, the approach reduces processor time and energy overheads by 76%-100% and 49%-79% respectively.

Hibernus++: A Self-Calibrating and Adaptive System for Transiently-Powered Embedded Devices

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass, and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This paper proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over state-of-the-art approaches.

Graceful Performance Modulation for Power-Neutral Transient Computing Systems

Transient computing systems do not have energy storage, and operate directly from energy harvesting. These systems are often faced with the inherent challenge of low-current or transient power supply. In this paper, we propose “power-neutral” operation, a new paradigm for such systems, whereby the instantaneous power consumption of the system must match the instantaneous harvested power. Power neutrality is achieved using a control algorithm for dynamic frequency scaling, modulating system performance gracefully in response to the incoming power. Detailed system model is used to determine design parameters for selecting the system voltage thresholds where the operating frequency will be raised or lowered, or the system will be hibernated. The proposed control algorithm for power-neutral operation is experimentally validated using a microcontroller incorporating voltage threshold-based interrupts for frequency scaling. The microcontroller is powered directly from real energy harvesters; results demonstrate that a power-neutral system sustains operation for 4%-88% longer with up to 21% speedup in application execution.

Approaches to Transient Computing for Energy Harvesting Systems: A Quantitative Evaluation

Systems operating from harvested sources typically integrate batteries or supercapacitors to smooth out rapid changes in harvester output. However, such energy storage devices require time for charging and increase the size, mass and cost of the system. A recent approach to address this is to power systems directly from the harvester output, termed transient computing. To solve the problem of having to restart computation from the start due to power-cycles, a number of techniques have been proposed to deal with transient power sources. In this paper, we quantitatively evaluate three state-of-the-art approaches on a Texas Instruments MSP430 microcontroller characterizing the application scenarios where each performs best. Finally, recommendations are provided to system designers for selecting the most suitable approach.

Intermittently-powered energy harvesting step counter for fitness tracking

Over the past decade, there has been a rapid increase in the popularity of wearable and portable devices, such as step counters, to monitor fitness performance. However, these devices are battery-powered, meaning that their lifetimes are restricted by battery capacity. Ideally, wearable devices could be powered by energy harvested from human motion. Energy harvesting systems traditionally incorporate energy storage to cope with source variability. However, energy storage takes time to charge and increases the size and cost of systems. This paper proposes an intermittently-powered energy harvesting step counter for integrated wearable applications, which aims to remove the energy storage element. The proposed step counter sustains its operation by harvesting energy from footsteps using a ferroelectret insole, which also works as an event detection sensor, i.e. the system is powered by the parameter that is being sensed. Designing this required the characterization of the insole to evaluate the amount of energy provided, and analysis of the energy needed by the overall system. Finally, the system was implemented and experimentally validated. The proposed step counter has an error of less than 4% when walking, which is lower than the error in conventional smartphone applications.

Wearable and autonomous computing for future smart cities: Open challenges

The promise of smart cities offers the potential to change the way we live, and refers to the integration of IoT systems for people-centred applications, together with the collection and processing of data, and associated decision making. Central to the realization of this are wearable and autonomous computing systems. There are considerable challenges that exist in this space that require research across different areas of electronics and computer science; it is this multidisciplinary consideration that is novel to this paper. We consider these challenges from different perspectives, involving research in devices, infrastructure and software. Specifically, the challenges considered are related to IoT systems and networking, autonomous computing, wearable sensors and electronics, and the coordination of collectives comprising human and software agents.

Thermally-aware composite run-time CPU power models

Accurate and stable CPU power modelling is fundamental in modern system-on-chips (SoCs) for two main reasons: 1) they enable significant online energy savings by providing a run-time manager with reliable power consumption data for controlling CPU energy-saving techniques; 2) they can be used as accurate and trusted reference models for system design and exploration. We begin by showing the limitations in typical performance monitoring counter (PMC) based power modelling approaches and illustrate how an improved model formulation results in a more stable model that efficiently captures relationships between the input variables and the power consumption. Using this as a solid foundation, we present a methodology for adding thermal-awareness and analytically decomposing the power into its constituting parts. We develop and validate our methodology using data recorded from a quad-core ARM Cortex-A15 mobile CPU and we achieve an average prediction error of 3.7% across 39 diverse workloads, 8 Dynamic Voltage-Frequency Scaling (DVFS) levels and with a CPU temperature ranging from 31° C to 91° C. Moreover, we measure the effect of switching cores offline and decompose the existing power model to estimate the static power of each CPU and L2 cache, the dynamic power due to constant background (BG) switching, and the dynamic power caused by the activity of each CPU individually. Finally, we provide our model equations and software tools for implementing in a run-time manager or for using with an architectural simulator, such as gem5.

Exploring ARM mbed support for transient computing in energy harvesting IoT systems

Energy 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.

Power neutral performance scaling for energy harvesting MP-SoCs

Using energy ‘harvested’ from the environment to power autonomous embedded systems is an attractive ideal, alleviating the burden of periodic battery replacement. However, such energy sources are typically low-current and transient, with high temporal and spatial variability. To overcome this, large energy buffers such as supercapacitors or batteries are typically incorporated to achieve energy neutral operation, where the energy consumed over a certain period of time is equal to the energy harvested. Large energy buffers, however, pose environmental issues in addition to increasing the size and cost of systems. In this paper we propose a novel power neutral performance scaling approach for multiprocessor system-on-chips (MP-SoCs) powered by energy harvesting. Under power neutral operation, the systems performance is dynamically scaled through DVFS and DPM such that the instantaneous power consumption is approximately equal to the instantaneous harvested power. Power neutrality means that large energy buffers are no longer required, while performance scaling ensures that available power is effectively utilised. The approach is experimentally validated using the Samsung Exynos5422 big.LITTLE SoC directly coupled to a monocrystalline photovoltaic array, with only 47mF of intermediate energy storage. Results show that the proposed approach is successful in tracking harvested power, stabilising the supply voltage to within 5% of the target value for over 93% of the test duration, resulting in the execution of 69% more instructions compared to existing static approaches.

Ultra-low power sensor for autonomous non-invasive voltage measurement in IoT solutions for energy efficiency

Monitoring current and voltage waveforms is fundamental to assess the power consumption of a system and to improve its energy efficiency. In this paper we present a smart meter for power consumption which does not need any electrical contact with the load or its conductors, and which can measure both current and voltage. Power metering becomes easier and safer and it is also self-sustainable because an energy harvesting module based on inductive coupling powers the entire device from the output of the current sensor. A low cost 32-bit wireless CPU architecture is used for data filtering and processing, while a wireless transceiver sends data via the IEEE 802.15.4 standard. We describe in detail the innovative contact-less voltage measurement system, which is based on capacitive coupling and on an algorithm that exploits two pre-processing channels. The system self-calibrates to perform precise measurements regardless the cable type. Experimental results demonstrate accuracy in comparison with commercial high-cost instruments, showing negligible deviations.