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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]

In the era of widespread edge computing, energy conservation modes like complete power shutdown are crucial for battery-powered devices, but they risk data loss in volatile memory. Energy autonomous systems, relying on ambient energy, face operational challenges due to power losses. Recent advancements in emerging nonvolatile memories (NVMs) like FRAM, RRAM, MRAM, and PCM offer mature solutions to sustain work progress with minimal energy overhead during outages. This paper thoroughly reviews utilizing emerging NVMs in microcontroller units (MCUs), comparing their key attributes to describe unique benefits and potential applications. Furthermore, we discuss the intricate details of NVM circuit design and NVM-driven compute-in-memory (CIM) architectures. In summary, integrating emerging NVMs into MCUs showcases promising prospects for next-generation applications such as Internet of Things and neural networks. [ABSTRACT FROM AUTHOR]

Beyond-edge devices can operate outside the reach of the power grid and without batteries. Such devices can be deployed in large numbers in regions that are difficult to access. Using machine learning, these devices can solve complex problems and relay valuable information back to a host. Many such devices deployed in low Earth orbit can even be used as nanosatellites. Due to the harsh and unpredictable nature of the environment, these devices must be highly energy-efficient, be capable of operating intermittently over a wide temperature range, and be tolerant of radiation. Here, we propose a non-volatile processing-in-memory architecture that is extremely energy-efficient, supports minimal overhead checkpointing for intermittent computing, can operate in a wide range of temperatures, and has a natural resilience to radiation. [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]

The last decade has seen significant advances in power optimization for IoT sensors. The conventional wisdom considers that if we reduce the power consumption of each component (e.g., processor, radio) into μ W-level of power, the IoT sensors could achieve overall ultra-low power consumption. However, we show that this conventional wisdom is overturned, as bus communication can take significant power for exchanging data between each component. In this paper, we analyze the power efficiency of bus communication and ask whether it is possible to reduce the power consumption for bus communication. We observe that existing bus architectures in mainstream IoT devices can be classified into either push-pull or open-drain architecture. push-pull only adapts to unidirectional communication, whereas open-drain inherently fits for bidirectional communication which benefits simplifying bus topology and reducing hardware costs. However, open-drain consumes more power than push-pull due to the high leakage current consumption while communicating on the bus. We present Turbo, a novel approach introducing low power to the open-drain based buses by reducing the leakage current created on the bus. We instantiate Turbo on I 2 C bus and evaluate it with commercial off-the-shelf (COTS) sensors. The results show a 76.9 % improvement in power efficiency in I 2 C communication. [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]

For decades, the design of untethered devices has been focused on delivering a fixed quality of service with minimum power consumption, to enable battery-powered devices with reasonably long deployment lifetime. However, to realize the promised tens of billions of connected devices in the Internet of Things, computers must operate autonomously and harvest ambient energy to avoid the cost and maintenance requirements imposed by mains- or battery-powered operation. But harvested power typically fluctuates, often unpredictably, and with large temporal and spatial variability. Energydriven computers are designed to treat energyavailability as a first-class citizen, in order to gracefully adapt to the dynamics of energy harvesting. They may sleep through periods of no energy, endure periods of scarce energy, and capitalize on periods of ample energy. In this paper, we describe the promise and limitations of energydriven computing, with an emphasis on intermittent operation. [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]

Recent research has looked to supplement or even replace the batteries in embedded computing systems with energy harvesting, where energy is derived from the device's environment. However, such supplies are generally unpredictable and highly variable, and hence systems typically incorporate large external energy buffers (e.g., supercapacitors) to sustain computation; however, these pose environmental issues and increase system size and cost. This article proposes Momentum, a general power-neutral methodology, with intrinsic system-wide maximum power point tracking, that can be applied to a wide range of different computing systems, where the system dynamically scales its performance (and hence power consumption) to optimize computational progress depending on the power availability. Momentum enables the system to operate around an efficient operating voltage, maximizing forward application execution, without adding any external tracking or control units. This methodology combines at runtime (1) a hierarchical control strategy that utilizes available power management controls (such as dynamic voltage and frequency scaling, and core hot-plugging) to achieve efficient power-neutral operation; (2) a software-based maximum power point tracking scheme (unlike existing approaches, this does not require any additional hardware), which adapts the system power consumption so that it can work at the optimal operating voltage, considering the efficiency of the entire system rather than just the energy harvester; and (3) experimental validation on two different scales of computing system: a low power microcontroller (operating from the already-present 4.7µF decoupling capacitance) and a multi-processor system-on-chip (operating from 15.4mF added capacitance). Experimental results from both a controlled supply and energy harvesting source show that Momentum operates correctly on both platforms and exhibits improvements in forward application execution of up to 11% when compared to existing power-neutral approaches and 46% compared to existing static approaches. [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]

Energy harvesting is currently a hot research topic, mainly as a consequence of the increasing attractiveness of computing and sensing solutions based on small, low-power distributed embedded systems. Harvesting may enable systems to operate in a deploy-and-forget mode, particularly when power grid is absent and the use of rechargeable batteries is unattractive due to their limited lifetime and maintenance requirements. This paper focuses on wind flow as an energy source feasible to meet the energy needs of a small autonomous embedded system. In particular the contribution is on the electrical converter and system integration. We characterize the micro-wind turbine, we define a detailed model of its behaviour, and then we focused on a highly efficient circuit to convert wind energy into electrical energy. The optimized design features an overall volume smaller than 64 cm3. The core of the harvester is a high efficiency buck-boost converter which performs an optimal power point tracking. Experimental results show that the wind generator boosts efficiency over a wide range of operating conditions. [ABSTRACT FROM AUTHOR]

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]