Intermittent systems powered by harvested energy frequently encounter power outages, requiring efficient mechanisms to save and restore their internal state, to ensure computational progress. In these systems, minimising the overhead of state retention, comprising CPU core registers and main volatile memory contents (a snapshot), is essential to maximise computational progress within the constraints of limited energy availability. This paper introduces a hardware module,MeTra(MEmory TRAcing), designed to enhance energy-efficient state retention in intermittent systems. This is achieved by tracing and selectively saving the modified parts of the main volatile memory (RAM) to non-volatile memory (NVM), i.e. FRAM. Additionally,MeTradynamically adjusts the voltage threshold that initiates state saving, optimizing energy usage for each snapshot and enabling the system to dedicate more harvested energy to useful computations.MeTrawas integrated with an Arm Cortex-M1 processor on an FPGA and evaluated using benchmarks including matrix multiplication, array sorting, and advanced encryption standard (AES), demonstrating its effectiveness in reducing state-saving time and improving energy efficiency by selectively saving modified RAM regions instead of the entire memory. Experimental results demonstrate that snapshot time can be reduced by 48.34% to 57.56% in FRAM-based systems, leading to an improvement in energy efficiency of 65.24% to 77.76%. These gains are achieved by selectively saving 5.66% to 19.82% of RAM, usingMeTra, compared to saving entire RAM.

The intermittent and varying nature of energy harvesting (EH) entails dedicated energy management with large energy storage, which is a limiting factor for low-power/cost systems with small form factors. Transient computing allows system operations to be performed in the presence of power outages by saving the system state into a non-volatile memory (NVM), thereby reducing the size of this storage. These systems are often designed with a task-based strategy, which requires the storage to be sized for the most energy consuming task. That is, however, not ideal for most systems since their tasks have varying energy requirements, i.e., energy storage size and operating voltage. Hence, to overcome this issue, this paper proposes a novel energy management unit (EMU), tailored for multi-source EH transient systems, that allows selecting the storage size and operating voltage for the next task to be performed at run-time, thereby optimizing task-specific energy needs and startup times based on application requirements. For the first time in literature, we adopted a hybrid NVM+VM approach allowing our EMU to reliably and efficiently retain its internal state, i.e., stateful EMU, under even the most severe EH conditions. Extensive empirical evaluations validated the operation of the proposed stateful EMU at a small overhead (0.07mJ of energy to update the EMU state and a$\simeq 4\mu\mathrm{A}$of static current consumption of the EMU).

In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer integrated switching and storage capabilities showing promising performance for ML applications. However, ReRAM devices have design challenges, such as nonlinear digital-analog conversion and circuit overheads. This paper proposes an In-Memory Boolean-to-Current Inference Architecture (IMBUE) that uses ReRAM-transistor cells to eliminate the need for such conversions. IMBUE processes Boolean feature inputs expressed as digital voltages and generates parallel current paths based on resistive memory states. The proportional column current is then translated back to the Boolean domain for further digital processing. The IMBUE architecture is inspired by the Tsetlin Machine (TM), an emerging ML algorithm based on intrinsically Boolean logic. The IMBUE architecture demonstrates significant performance improvements over binarized convolutional neural networks and digital TM in-memory implementations, achieving up to a 12.99x and 5.28x increase, respectively.

Many modern compute-intensive applications require arithmetic results (usually multiplication) to be represented as analog signals. Using digital multipliers followed by digital-to-analog conversion (DAC) results in high energy and performance costs. This is because digital multipliers have costly carry propagation, and DAC circuits add associated conversion costs. Another concern, especially for arithmetic on the edge, is the need for nonvolatile operands in the face of power uncertainty. To deal with this, nonvolatile memory technologies have been combined with in-memory computing. This paper proposes a mixed-signal multiplier which directly generates an analog product based on two digital input operands. Fundamental to the design are transistor-memristor cells, organized in a crossbar structure. Using analog resistive partial product accumulation in the crossbar, the approximate multiplier eliminates the need for carry propagation and an explicit DAC. It also provides asymmetric nonvolatility making memristor writing a rare event, extending the application significance of the method. The design is shown to be functionally correct up to 4-bit, and achieves 8× to over 300× speedup, competitive peak-power and orders of magnitude energy reduction, compared with existing full-digital memristor-based multipliers and low-power multiplication DAC solutions.

Residential water meters accommodate various methods of power provisioning. Electromagnetic and ultrasonic meters, for example, often rely on a battery-like external power source, whereas mechanical meters harvest energy from water flow through an impeller. Although energy harvesting (EH) minimizes maintenance needs driven by battery depletion/replenishment, placing a physical element into the flow adversely affects water pressure. This intrusive EH/sensing technique is not user-friendly either since the meters with impellers need to be embedded into pipes by skilled personnel. Hence, this paper proposes a non-intrusive sensor system powered by thermoelectric generators (TEGs) forplug-and-playwater flow rate measurement. This system, equipped with a custom-made energy management unit (EMU), adopts ultrasonic sensors, a task-based computing scheme, and a LoRa module for autonomous sensing and reporting of the flow rate. After summarizing thermoelectricity and delta time-of-flight ($\Delta$ToF)-based ultrasonic sensing theory, we provide the system model and design details with a particular focus on the EMU. Then, we experimentally evaluate the system under varying conditions, demonstrating their impact on average sensing and transmission periods. The results unveil that our proposal can achieve high measurement precision ($\pm 1.4\%$), comparable to its intrusive and battery-powered counterparts, and thus has the potential of replacing the residential water meters.

Multipliers are a major energy and delay contributor in modern compute-intensive applications due to their complex logic architecture. As such, designing multipliers with reduced energy and faster speed has remained a thoroughgoing challenge. This paper presents a novel, carry-free multiplier, which is suitable for a new-generation of energy-constrained applications. The multiplier circuit consists of an array of memristor-transistor cells that can be selected (i.e., turned ON or OFF) using a combination of DC bias voltages based on the operand values. When a cell is selected it contributes to current in the array path, which is then amplified by current mirrors with variable transistor gate sizes. The different current paths are connected to a node for analogously accumulating the currents to produce the multiplier output directly. This removes the need for latency-sensitive carry propagation stages, typically seen in traditional multipliers. We conduct a number of experiments to validate the functional and parametric properties. Our experiments showed that proposed multiplier achieves 51.44% savings in energy at a similar accuracy when compared with recently proposed approaches.

Reactive transient computing systems preserve computational progress despite frequent power failures by suspending (saving state to nonvolatile memory) when detecting a power failure, and restoring once power returns. Existing methods inefficiently save and restore all allocated memory. We propose lightweight memory management that applies the concept of paging to load pages only when needed, and save only modified pages. We then develop a model that maximises available execution time by dynamically adjusting the suspend and restore voltage thresholds. Experiments on an MSP430FR5994 microcontroller show that our method reduces state retention overheads by up to 86.9% and executes algorithms up to 5.3× faster than the state-of-the-art.

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.

This paper presents the implementation of a bicycle trip counter, which measures cycling speed, traveled distance, and cycling time, that is directly powered from tiny periodic pulses of energy with only the intrinsically present decoupling capacitance as an energy buffer. To cope with the highly variable amount of energy generated during each pulse, an energy-driven approach is used. The core principles in this approach are to dynamically adjust operational mode according to energy availability, to scale performance, for example sensing accuracy, proportional to energy harvested, and to perform intermittent or transient computing to enable computation across multiple power cycles. The device presented is able to start operation from energy supply pulses as low as 4 uJ, where a rough estimate of the sensing parameters is done, and perform increasingly complex and time-consuming tasks such as additional more accurate measurements, sensor fusion, and filtering computations as more energy becomes available.

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 system's 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.

Energy harvesting is an efficient solution to power embedded systems instead of using batteries. However, it has been traditionally coupled with large energy buffers to tackle the temporal variation of the source. These buffers require time to charge and introduce a cost, size and weight overhead. Energy-driven and transiently-powered systems can operate from an energy harvesting source, while containing little or no additional energy storage. However, few real-life applications have been considered for such systems to demonstrate that they can actually be realised. This poster presents a transiently-powered wireless bicycle trip counter which measures distance, speed and active cycling time, and transmits data wirelessly. The system sustains operation by harvesting energy from the rotation of the wheel, operating from 6kph.

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.

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.

The recent research efforts in smart grids and residential power management are oriented to monitor pervasively the power consumption of appliances in domestic and non-domestic buildings. Knowing the status of a residential grid is fundamental to keep high reliability levels while real time monitoring of electric appliances is important to minimize power waste in buildings and to lower the overall energy cost. Wireless Sensor Networks (WSNs) are a key enabling technology for this application field because they consist of low-power, non-invasive and cost-effective intelligent sensor devices. We present a wireless current sensor node (WCSN) for measuring the current drawn by single appliances. The node can self-sustain its operations by harvesting energy from the monitored current. Two AAA batteries are used only as secondary power supply to guarantee a fast start-up of the system. An active ORing subsystem selects automatically the suitable power source, minimizing power losses typical of the classic diode configuration. The node harvests energy when the power consumed by the device under measurement is in the range 10W÷10kW, which also corresponds to the range of current 50mA÷50A drawn directly from the main. Finally the node features a low-power, 32-bit microcontroller for data processing and a wireless transceiver to send data via the IEEE 802.15.4 standard protocol.