Power Consumption Research Articles

A synapse with low power consumption based on MoTe2/SnS2heterostructure.

The use of two-dimensional materials and van der Waals heterostructures holds great potential for improving the performance of memristors Here, we present SnS2/MoTe2heterostructure synaptic transistors. Benefiting from the ultra-low dark current of the heterojunction, the power consumption of the synapse is only 19 pJ per switching under 0.1 V bias, comparable to that of biological synapses. The synaptic device based on the SnS2/MoTe2demonstrates various synaptic functionalities, including short-term plasticity, long-term plasticity, and paired-pulse facilitation (PPF). In particular, the synaptic weight of the excitatory postsynaptic current (EPSC) can reach 109.8%. In addition, the controllability of the long-term potentiation (LTP) and long-term depression (LTD) are discussed. The dynamic range (Gmax/Gmin) and the symmetricity values of the synaptic devices are approximately 16.22 and 6.37, and the non-linearity is 1.79. Our study provides the possibility for the application of 2D material synaptic devices in the field of low-power information storage.
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Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain–computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks (SNNs) have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10 ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic SNNs make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.

In situ cleaning of foulants by gas scouring on the membrane-electrode in an electro-membrane bioreactor.

Low-maintenance membrane cleaning is essential for the stable operation of membrane bioreactors. This work proposes an in-situ electrical-cleaning method using an electro-MBR. When the applied bias was transiently increased, the membrane flux recovered rapidly because of the scouring effect from gas evolution reactions. The exfoliation of the cake layer induced by gas scouring played a major role in mitigating membrane fouling, recovering the transmembrane pressure (TMP) by 88.6 % under optimal conditions. Membrane modules did not require replacement during the operation period due to the efficacy of electrical cleaning, with the TMP varying between 37.5 % and 62.5 % of the ultimate pressure requiring change of the membrane modules. Despite the increase in power consumption of 0.66 Wh·m-3 due to the additional applied bias, there was no need for chemical additives or manual maintenance. Therefore, the electrical cleaning method enhanced the service life and reduced the maintenance costs of the electro-MBR.

(Invited) Ferroelectric Nonvolatile Capacitive Synapse for Charge Domain Compute-in-Memory

The ferroelectric field effect transistor (FeFET) can be configured in nvCAP mode to enable a small-signal capacitive readout. Operating the FeFET in nvCAP mode for Charge-domain Compute-in-Memory provides benefits such as reduced power consumption and suppressed read disturb. In this review article, working of FeFET in nvCAP mode is described. Further, the TCAD study results are summarized to optimize the FeFET structure for maximizing the device performance in nvCAP mode, where the gate area and the overlap region decides the ON and OFF state capacitance. Next, the reliability aspects of nvCAP are discussed. The FeFET in nvCAP demonstrates an initial endurance of 106 P/E cycles, which can be further extended by 100× by performing a recovery operation. The nvCAP device shows a retention of at least 1 day at 850C for the fresh, fatigued and recovered state of the device, making it a suitable candidate for Compute-in-Memory.

A high current efficiency rail-to-rail operational amplifier

ABSTRACT This paper proposes a new single stage high current efficiency rail to rail amplifier (HCE-RTR). Due to the combination of a new constant transconductance control circuit and local common mode feedback (LCMFB) technique, the proposed rail to rail amplifier not only achieves improvements in unity-gain bandwidth (GBW) and slew rate (SR), but also ensures input stage transconductance constancy within the rail to rail voltage range. Based on the SMIC 180 nm process, the post-layout simulation results show that the proposed amplifier achieves rail-to-rail input/output. The simulated open loop AC response shows the DC gain, GBW, and PM are 66.2 dB, 8.2 MHz, and 60.97 o , respectively. When the driving load capacitance is 70 pF, the transient response simulation results show that the proposed amplifier achieves 0.039 µs average 1% settling time, and about 5.73 V/µs average slew rate. Note that under the condition of 1.8 V power supply, the power consumption is only 122.4 µW, proving the proposed amplifier can achieve high current efficiency while maintaining stability.

A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments.

The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.

Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain-Computer Interface.

This study aims to estimate the maximum power consumption that guarantees a thermally safe operation for a titanium-enclosed chest wall unit (CWU) subcutaneously implanted in the pre-pectoral area. This unit is a central piece of an envisioned fully-implantable bi-directional brain-computer interface (BD-BCI). To this end, we created a thermal simulation model using the finite element method implemented in COMSOL. We also performed a sensitivity analysis to ensure that our predictions were robust against the natural variation of physiological and environmental parameters. Based on this analysis, we predict that the CWU can consume between 378 and 538mW of power without raising the surrounding tissue's temperature above the thermal safety threshold of 2 C. This power budget should be sufficient to power all of the CWU's basic functionalities, which include training the decoder, online decoding, wireless data transmission, and cortical stimulation. This power budget assessment provides an important specification for the design of a CWU-an integral part of a fully-implantable BD-BCI system.

Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space.

Arthropods' eyes are effective biological vision systems for object tracking and wide field of view because of their structural uniqueness; however, unlike mammalian eyes, they can hardly acquire the depth information of a static object because of their monocular cues. Therefore, most arthropods rely on motion parallax to track the object in three-dimensional (3D) space. Uniquely, the praying mantis (Mantodea) uses both compound structured eyes and a form of stereopsis and is capable of achieving object recognition in 3D space. Here, by mimicking the vision system of the praying mantis using stereoscopically coupled artificial compound eyes, we demonstrated spatiotemporal object sensing and tracking in 3D space with a wide field of view. Furthermore, to achieve a fast response with minimal latency, data storage/transportation, and power consumption, we processed the visual information at the edge of the system using a synaptic device and a federated split learning algorithm. The designed and fabricated stereoscopic artificial compound eye provides energy-efficient and accurate spatiotemporal object sensing and optical flow tracking. It exhibits a root mean square error of 0.3 centimeter, consuming only approximately 4 millijoules for sensing and tracking. These results are more than 400 times lower than conventional complementary metal-oxide semiconductor-based imaging systems. Our biomimetic imager shows the potential of integrating nature's unique design using hardware and software codesigned technology toward capabilities of edge computing and sensing.

Performance Evaluation of Water Cooled Condenser of Air Conditioner Working on VCRS

VCRS commonly known as (Vapour Compression Refrigeration System) is widely adopted method for air conditioning of buildings, in pharmaceutical industry for storage of medicine, in hospitals, automobiles etc. There are several methods for increasing the efficiency% (COP) of an VCRS cycle. Some of the modification can be done in Compressor, Condenser, Evaporator, and in Refrigerant . In this experiment, we adopted method of Subcooling to reduce Compressor work. This study investigates the implementation of subcooling in the condenser of a Vapour Compression Refrigeration System (VCRS) to enhance its efficiency and reduce power consumption. The traditional condenser design is modified by submerging it in a water tank, allowing for subcooling of the refrigerant. The subcooling process involves lowering the temperature of the refrigerant below its saturation temperature, which improves the system’s Coefficient of Performance (COP) by increasing the density of the refrigerant entering the expansion valve. This modification aims to optimize the heat rejection process and enhance the overall performance of the refrigeration system .Experimental result demonstrate a significant reduction in power consumption and an improvement in COP, highlighting the effectiveness of the condenser subcooling method in enhancing the efficiency of VCRS. Keywords : Compressor, Condenser, Expansion Valve, Evaporator.

ENHANCING E –LIFE WITH IOT: A SMART APPROACH TO EFFICIENT ELECTRICITY MANAGEMENT

In the contemporary era, energy conservation stands out as a pivotal challenge in striving towards a sustainable lifestyle. Regrettably, it often falls by the wayside due to a lack of awareness and the uncertainty surroundings the lifespan of electrical and electronic equipment. Nonetheless, energy conservation remains the most economically viable approach to address energy deficits. Introducing “E-Life,” a ground breaking product, offers a solution to this dilemma while fostering public consciousness. Traditional electricity-intensive devices, despite having specified power requirements and lifespans, frequently outlast their projected longevity. However, they tend to consume more power than indicated due to aging effects. For instance, empirical investigations reveal that a 40-watt incandescent light consumes an additional 25% of its original power post- lifespan. To mitigate such inefficiencies and monitor excessive power consumption across all electronic equipment, we propose integrating the E-life algorithm into an IoT-enabled device. This device, based on the ESP32 platform and capable of managing two loads, provides real-time monitoring and prompts replacement when necessary. Experimental results demonstrate that this innovative gadget can yield energy savings of over 37% within a six-month timeframe for constrained loads. Keywords—Energy Saving; Efficiency; Energy Management; Energy Conservation; IoT; E- Life; ESP32

Homogeneous and Heterogeneous Multicore Systems

Multicore systems have gained significant importance in the Automotive industry due to data- intensive applications, such as image processing, high- speed process, and GPS application. It enables manufacturers to build smaller chips, simplifying board architecture and routing, reducing power consumption and cost and increasing programmability. Multicore platforms are segregated into two categories namely Homogeneous (symmetric) and Heterogeneous (asymmetric) multicore systems. In Homogeneous systems, all cores are the same, including frequencies, cache sizes and functions. In Heterogeneous systems, different cores operate with different frequencies, cache sizes and functions. In this paper, we are going to discuss various aspects of homogeneous and heterogeneous multicore architectures like number and level of caches, interconnection of the cores, Physical and Temporal isolation, Energy Efficiency, Concurrency, Performance, Reliability and Robustness along with the evaluation of these two architectures for applications based on the above-mentioned aspects.

Visible-light-driven BiOI and GO/BiOI photocatalysts for organic pollutants degradation and hydrogen production using low power sources

BiOI and (3 wt%)GO/BiOI visible-light-driven photocatalysts were obtained by a one-pot solvothermal method and successfully applied to the degradation of single and binary dye solutions of rhodamine B (RhB) and methylene blue (MB) and H2 production using very low-power sources. The GO/BiOI with hierarchical flower morphologies exhibited the highest activity, achieving RhB and MB photodegradation percentages (%Xdye) of 100% and 80%, respectively, in 240 min employing a simple 19 W white LED array. Furthermore, GO/BiOI dosage and RhB initial concentration play an essential role in dye degradation, and scavenger assays confirmed that holes and superoxides are the main species causing RhB oxidation. TOC analysis determined an efficiency of 70%, and after three uses, the GO/BiOI attained a %XRhB of 84%. The H2 evolution was performed under 2 W UV light, yielding 323.25 μmol/h∙g of H2 for BiOI, and the addition of GO nanosheets increased the photoactivity of GO/BiOI up to 63% (509.61 μmol/h∙g). The catalytic activity of GO/BiOI is superior to values reported in the literature considering nominal power consumption (kWh) vs. efficiency of RhB degradation or H2 production.

Heterocycle- and Amine-Free Electrochromic and Electrofluorochromic Molecules for Energy-Saving See-Through Smart Windows and Displays.

Electrochromic (EC) smart windows are an elegant alternative to dusty curtains, blinds, and traditional dimming devices. The EC energy storage smart windows and displays received remarkable attention in the optoelectronic industry as they hold promise for high energy efficiency, low power consumption, reversibility, and swift response to stimuli. However, achieving these properties remains challenging. Moreover, most EC molecules do not exhibit electrofluorochromism, which is highly essential for smart displays because its EC property can modulate the solar heat entering the building, and its electrofluorochromic (EFC) aspects can create lighting during the night. In this work, a structure-property relationship is utilized to develop new electrochromes that can store the injected charge, and these molecules indeed exhibit electrofluorochromism. The compounds are synthesized from tetrabenzofluorene with two aromatic acceptor units, and avoids the use of widely studied heterocycles and amine derivatives. The electrochromes switches from yellow to dark hue in solution, solid, and gel state. The compounds display exceptional electrochemical stability and reversibility in 1000 cycles and capacity retention of 93-100% in 300 charging-discharging cycles. The proof-of-concept device fabrication of the self-dimming EC smart window presented here demonstrates that it can furnish visual comfort, modulate transmitted light and glare, and reduce energy usage.

Estimation of Centrifugal Pump Efficiency at Variable Frequency for Irrigation Systems

The sustainability of the food production achieved with the help of irrigation systems and the sustainability of their energy consumption are major challenges of the current century. Pumping systems currently account for approximately 30% of global electrical energy consumption. As electricity prices rise, there is a growing need for technological advancements to enhance energy efficiency and reduce consumption costs effectively. This study focuses on operating centrifugal pumps at variable frequency as an effective means of achieving this goal. Most centrifugal pump manufacturers/providers traditionally assume that pump efficiency remains constant across various operating frequencies, often equating the efficiency at various frequencies to that at the standard frequency (50/60 Hz). In contrast, this paper introduces a new formula for estimating pump efficiency, crucial to precise power consumption determination, particularly in variable-frequency scenarios. The formula parameters are identified by using experimental data acquired from an existing pumping system. The tests and results presented in this paper demonstrate that the proposed formula outperforms the formulas of the current industry standards in accuracy. Practically, the new relation assures enhanced accuracy in estimating pump efficiency and absorbed power, allowing for the design of a more precise model used for control systems synthesis required for operating centrifugal pumps at variable frequency. This research offers a new way of calculating pump efficiency, which could be very useful for industry practitioners seeking to optimize energy consumption in pumping systems.

Technical and financial feasibility of a chemicals recovery and energy and water production from a dairy wastewater treatment plant

Due to the high volume of wastewater produced from dairy factories, it is necessary to integrate a water recovery process with the treatment plant. Today, bipolar membrane electrodialysis units (BMEUs) are increasingly developed for wastewater treatment and reutilizing. This article aims to develop and evaluate (technical and cost analyses) a combined BMEU/batch reverse osmosis unit (BROU) process for the recovery of chemicals and water from the dairy wastewater plant. The combined BROU/BMEU process is able to simultaneously produce water and strong base-acid, and reduce power consumption due to the injection of concentrated feed flow into the BMEU. A comprehensive comparative analysis on the performances of two combined and stand-alone BMEU configurations are developed. The proposed combined technology for dairy factory wastewater treatment is designed on a new structure and configuration that can address superior cost analysis compared to similar technologies. Further, the optimal values of permeate flux and current density as two vital and influencing parameters on the performance of the studied dairy wastewater treatment process were calculated and discussed. From the outcomes, the total cost of production in the combined configuration has been reduced by approximately 26% compared to the stand-alone configuration. Increasing the feed concentration rate using the batch reverse osmosis process for the dairy wastewater treatment process can be an ideal solution from an economic point of view. Moreover, point (current density, feed concentration rate, total unit cost) = 328.9,7,14.37\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left(328.9, 7, 14.37\\right)$$\\end{document} can be considered as an optimal point for the economic performance of the studied wastewater treatment process.

Comprehensive Review of RF MEMS Switches in Satellite Communications

The miniaturization and low power consumption characteristics of RF MEMS (Radio Frequency Microelectromechanical System) switches provide new possibilities for the development of microsatellites and nanosatellites, which will play an increasingly important role in future space missions. This paper provides a comprehensive review of RF MEMS switches in satellite communication, detailing their working mechanisms, performance optimization strategies, and applications in reconfigurable antennas. It explores various driving mechanisms (electrostatic, piezoelectric, electromagnetic, thermoelectric) and contact mechanisms (capacitive, ohmic), highlighting their advantages, challenges, and advancements. The paper emphasizes strategies to enhance switch reliability and RF performance, including minimizing the impact of shocks, reducing driving voltage, improving contacts, and appropriate packaging. Finally, it discusses the enormous potential of RF MEMS switches in future satellite communications, addressing their technical advantages, challenges, and the necessity for further research to optimize design and manufacturing for broader applications and increased efficiency in space missions. The research findings of this review can serve as a reference for further design and improvement of RF MEMS switches, which are expected to play a more important role in future aerospace communication systems.

Web-Based Digital Cell Library Characterization System

Web-Based Digital Cell Library Characterization System

Development of multiple input supply based modified SEPIC DC–DC converter for efficient management of DC microgrid

The development of DC microgrids is reliant on multi-input converters, which offer several advantages, including enhanced DC power generation and consumption efficiency, simplified quality, and stability. This paper describes the development of a multiple input supply based modified SEPIC DC–DC Converter for efficient management of DC microgrid that is powered by two DC sources. Here Multi-Input SEPIC converter offers both versatility in handling output voltage ranges and efficiency in power flow, even under challenging operating conditions like lower duty cycle values. These features contribute to the converter's effectiveness in managing power within a DC microgrid. In this configuration, the DC sources can supply energy to the load together or separately, depending on how the power switches operate. The detailed working states with equivalent circuit diagrams and theoretical waveforms, under steady-state conditions, are shown along with the current direction equations. This paper also demonstrates the typical analysis of large-signal, small-signal, steady-state modeling techniques and detailed design equations. The proposed configuration is validated through the conceptual examination using theoretical and comprehensive MATLAB simulation results. Detailed performance analysis has been done for different cases with various duty ratios. Finally, to show the competitiveness, the multi-input SEPIC topology is compared with similar recent converters.

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

Abstract Non-Intrusive Load Monitoring (NILM) identifies device power consumption or on/off states solely based on total power data, which is highly valuable for consumers to understand their appliance usage behavior and take necessary measures to reduce energy consumption, especially for the benefit of energy consumers' living production. However, a challenge faced by NILM is the tendency to focus excessively on power disaggregation while neglecting the disaggregation of on/off states, leading to lower classification accuracy, particularly owning to imbalanced states. This study proposes a model that integrates the power and on/off states to simultaneously disaggregate the power and device on/off states. The model comprises two main modules: a power encoding module for power disaggregation, and a convolutional state module for on/off state disaggregation. The power encoding module utilizes BERT-LSTM and Long Short-Term Memory (LSTM) networks for initial energy disaggregation. In contrast, the convolutional state module employs Convolutional Neural Networks (CNNs) for device state disaggregation. The output of the power-encoding module is multiplied by the probability of on/off states to obtain the final power.The proposed model is evaluated using the REDD and UK-DALE datasets. Compared to the baseline models, the results show an improvement in the device state classification average accuracy from 0.948 to 0.957, and a decrease in the average error between the real power and disaggregated power from 26.356W to 25.108W. Additionally, real-world experiments conducted using the designed platform for collecting and disaggregating power data achieve an average accuracy of 0.997. The proposed model demonstrates competitiveness in the NILM field and underscores its significance in aiding energy-consumption reduction efforts.

On-chip tunable quantum interference in a lithium niobate-on-insulator photonic integrated circuit

On-chip tunable quantum interference in a lithium niobate-on-insulator photonic integrated circuit