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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. [ABSTRACT FROM AUTHOR]

Frequent power failures can introduce significant packet losses during communication among energy harvesting batteryless wireless sensors. Nodes should be aware of the energy level of their neighbors to guarantee the success of communication and avoid wasting energy. This paper presents TRAP (TRAnsiently-powered Protocol) that allows nodes to communicate only if the energy availability on both sides of the communication channel is sufficient before packet transmission. TRAP relies on a novel modulator circuit, which operates without microcontroller intervention and transmits the energy status almost for free over the radiofrequency backscatter channel. Our experimental results showed that TRAP avoids failed transmissions introduced by the power failures and ensures reliable intermittent communication among batteryless sensors. [ABSTRACT FROM AUTHOR]

Emerging applications for Internet of Things (IoT) devices demand smaller mass, size, and cost whilst increasing capability and reliability. Energy harvesting can provide power to these ultra-constrained devices, but introduces unreliability, unpredictability, and intermittency. Schemes for wireless sensors without batteries or supercapacitors overcome intermittency through saving system state into nonvolatile memory before the supply drops below the minimum operating voltage, termed transient, or intermittent computing. However, this introduces significant time and energy overheads. This article presents two schemes that significantly reduce these overheads: entering a sleep mode to avoid saving state and utilizing direct memory access (DMA) when state saves are required. Time and energy previously wasted on state saves can instead be used to perform useful computation, termed “forward progress.” We practically validate the proposed approaches across a range of energy sources and IoT benchmarks and demonstrate up to 46.8% and 40.3% increase in forward progress and up to 91.1% and 85.6% reduction in overheads for each scheme, respectively. [ABSTRACT FROM AUTHOR]

Owing to its unique capability to sustain computation progress over power outages, a nonvolatile processor (NVP) is promising for energy-harvesting-powered Internet-of-Things devices. However, the widespread application of NVP is continually blocked by the system integration issues and the configuration overheads of peripheral devices. This paper presents a nonvolatile system-on-chip (NVSoC) with improved integration level, power management flexibility, and system wake-up speed. An on-chip power management subsystem is designed to minimize the number of external components while supporting versatile power policies. And a direct peripheral restore architecture is outlined, which enables a fast and parallel re-configuration of peripheral devices after the resumption of power supply. A test chip is fabricated in a 130-nm ferroelectric-CMOS process with 22.09-mm2 area. Measurement results show 6 $\times $ higher data throughput as compared with a conventional NVP when facing power failures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

The research on cyber-physical systems (CPSs) addresses the close interactions between the embedded cyber components and the dynamic physical components that could involve mechanical components, energy systems, human activities, and surrounding environment. Some example CPSs include automotive systems, energy systems, robot systems, and cyber-physical biochip systems. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM AUTHOR]