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 (DFS), 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

Energy-Driven Computing: Rethinking the Design of Energy Harvesting Systems

Energy harvesting computing has been gaining increasing traction over the past decade, fueled by technological developments and rising demand for autonomous and battery-free systems. Energy harvesting introduces numerous challenges to embedded systems but, arguably the greatest, is the required transition from an energy source that typically provides virtually unlimited power for a reasonable period of time until it becomes exhausted, to a power source that is highly unpredictable and dynamic (both spatially and temporally, and with a range spanning many orders of magnitude). The typical approach to overcome this is the addition of intermediate energy storage/buffering to smooth out the temporal dynamics of both power supply and consumption. This has the advantage that, if correctly sized, the system ‘looks like’ a battery-powered system; however, it also adds volume, mass, cost and complexity and, if not sized correctly, unreliability. In this paper, we consider energy-driven computing, where systems are designed from the outset to operate from an energy harvesting source. Such systems typically contain little or no additional energy storage (instead relying on tiny parasitic and decoupling capacitance), alleviating the aforementioned issues. Examples of energy-driven computing include transient systems (which power down when the supply disappears and efficiently continue execution when it returns) and power-neutral systems (which operate directly from the instantaneous power harvested, gracefully modulating their consumption and performance to match the supply). In this paper, we introduce a taxonomy of energy-driven computing, articulating how power-neutral, transient, and energy-driven systems present a different class of computing to conventional approaches

Energy-driven computing for energy-harvesting embedded systems

There has been increasing interest over the last decade in the powering of embedded systems from ‘harvested’ energy, and this has been further fuelled by the promise and vision of IoT. Energy harvesting systems present numerous challenges, although some of these are also posed by their battery-powered counterparts: e.g. ultra-low power consumption. However, a significant challenge not witnessed in battery-powered systems is a requirement to manage the combination of a highly unpredictable and variable (spatially and temporally) power supply with a highly dynamic (across many orders of magnitude) and often event-driven system power consumption. This problem is typically rectified through the addition of energy storage (e.g. a supercapacitor) to provide energy buffering to smooth out the dynamics of supply and consumption. This has the significant advantage of making the system ‘look like’ a battery-powered system, yet usually adds volume, mass and cost to the resultant system – something that is counterproductive in future flexible, wearable and implantable IoT systems. Such systems can, alternatively, include only a very small amount (or even zero) energy-storage. Now, instead of the system’s operation being dictated solely by the application, operation starts to become ‘energy-driven’, with execution being highly intertwined with power and energy availability. In this presentation, I will first introduce the landscape of energy-harvesting computing systems, and articulate how energy-driven computing presents a different class of computing to conventional approaches. A significant issue in the successful operation of these systems is their ability to operate from an intermittent, constrained and variable supply, and I will show how transient operation and power-neutrality can be used to achieve the vision for these systems, and hence enable the proliferation of tiny self-powered systems that will underpin much of the IoT

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 work 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 stateof- the-art approaches

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

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section

Building a Spiking Neural Network Model of the Basal Ganglia on SpiNNaker

We present a biologically-inspired and scalable model of the Basal Ganglia (BG) simulated on the SpiNNaker machine, a biologically-inspired low-power hardware platform allowing parallel, asynchronous computing. Our BG model consists of six cell populations, where the neuro-computational unit is a conductance-based Izhikevich spiking neuron; the number of neurons in each population is proportional to that reported in anatomical literature. This model is treated as a single-channel of action-selection in the BG, and is scaled-up to three channels with lateral cross-channel connections. When tested with two competing inputs, this three-channel model demonstrates action-selection behaviour. The SpiNNaker-based model is mapped exactly on to SpineML running on a conventional computer; both model responses show functional and qualitative similarity, thus validating the usability of SpiNNaker for simulating biologically-plausible networks. Furthermore, the SpiNNaker-based model simulates in real time for time-steps 1 ms; power dissipated during model execution is & #x2248;1.8 W

Transient force atomic force microscopy: systems approaches to emerging applications

In existing dynamic mode operation of Atomic Force Microscopes (AFMs) steady-state signals like amplitude and phase are used for detection and imaging of material. Due to the high quality factor of the cantilever probe the corresponding methods are inherently slow. In this dissertation, a novel methodology for fast interrogation of material that exploits the transient part of the cantilever motion is developed. This method effectively addresses the perceived fundamental limitation on bandwidth due to high quality factors. It is particularly suited for the detection of small time scale tip-sample interactions. Analysis and experiments show that the method results in significant increase in bandwidth and resolution as compared to the steady-state-based methods;In atomic force microscopy, bandwidth or resolution can be affected by active quality factor (Q) control. However, in existing methods the trade off between resolution and bandwidth remains inherent. Observer based Q control method provides greater flexibility in managing the tradeoff between resolution and bandwidth during imaging. It also facilitates theoretical analysis lacking in existing methods;In this dissertation we develop a method for exact constructive controllability of quantum-mechanical systems. The method has three steps, first a path from the initial state to the final state is determined and intermediate points chosen such that any two consecutive points are close, next small sinusoidal control signals are used to drive the system between the points, and finally quantum measurement technique is used to exactly achieve the desired state. The methodology is demonstrated for the control of spin-half particles in a Stern-Gerlach setting;In this dissertation, a novel closed-loop real-time scheduling algorithm is developed based on dynamic estimation of execution time of tasks based on both deadline miss ratio and task rejection ratio in the system. This approach is highly preferable for firm/soft real-time systems since it provides a firm performance guarantee in terms of high guarantee ratio. Proportional-integral controller and H-infinity controller are designed for closed loop scheduling. Simulation studies showed that closed-loop dynamic scheduling offers a better performance over the openloop scheduling under all the practical conditions

Optimizing Embedded Software of Self-Powered IoT Edge Devices for Transient Computing

IoT edge computing becomes increasingly popular as it can mitigate the burden of cloud servers significantly by offloading tasks from the cloud to the edge which contains the majority of IoT devices. Currently, there are trillions of edge devices all over the world, and this number keeps increasing. A vast amount of edge devices work under power-constrained scenarios such as for outdoor environmental monitoring. Considering the cost and sustainability, in the long run, self-powering through energy harvesting technology is preferred for these IoT edge devices. Nevertheless, a common and critical drawback of self-powered IoT edge devices is that their runtime states in volatile memory such as SRAM will be lost during the power outage. Thanks to the state-of-the-art non-volatile processor (NVP), the runtime volatile states can be saved into the on-chip non-volatile memory before the power outage and recovered when harvesting power becomes available. Yet the potential of a self-powered IoT edge device is still hindered by the intrinsic low energy efficiency and reliability. In order to fully exert the potentials of existing self-powered IoT edge devices, this dissertation aims at optimizing the energy efficiency and reliability of self-powered IoT edge devices through several software approaches. First, to prevent execution progress loss during the power outage, NVP-aware task schedulers are proposed to maximize the overall task execution progress especially for the atomic tasks of which the unfinished progress is subjected to loss regardless of having been checkpointed. Second, to minimize both the time and energy overheads of checkpointing operations on non-volatile memory, an intelligent checkpointing scheme is proposed which can not only ensure a successful checkpointing but also predict the necessity of conducting checkpointing to avoid excessive checkpointing overhead. Third, to avoid inappropriate runtime CPU clock frequency with low energy utility, a CPU frequency modulator is proposed which adjusts the runtime CPU clock frequency adaptively. Finally, to thrive in ultra-low harvesting power scenarios, a light-weight software paradigm is proposed to help maximize the energy extraction rate of the energy harvester and power regulator bundle. Besides, checkpointing is also optimized for more energy-efficient and light-weight operation