Reduce Power Consumption Research Articles

Pre-pulse-driven temperature modulation based on semiconductor gas sensor: A low-temperature strategy for gas detection

Semiconductor gas sensor has the advantages of low cost, simple detection principle and fast detection speed, so it has been widely used in gas detection in industrial processes. Sensors are usually maintained at high operating temperatures to maintain high performance, which is not only high power consumption but also the risk of explosion when the target gas concentration is too high. In this paper, the method of pre-pulse-driven temperature modulation is proposed to solve this problem. The sensor structure with ceramic-based micro-hot plate substrate has faster temperature change speed, which lays the foundation for high response. This method keeps the sensor operating at low temperature and obtains high response when it is in contact with the target gas. In addition, it is accompanied by the reduction of power consumption and the improvement of response speed. In this paper, SnO2 sensor is used to verify the effectiveness of the pre-pulse-drivn temperature modulation strategy, and then In2O3 and ZnO sensors are used to verify its universality. Finally, the effect of oxygen accumulation on the sensitization of pre-pulse-driven temperature modulation method is analyzed. It has been verified that pre-pulse-driven is an effective method to reduce temperature and improve sensitivity, which is worthy of promotion and further optimization.

Performance analysis and comparison of a novel Steiner-Quadrature Space Shift Keying (S-QSSK) scheme in massive MIMO systems

This paper introduces Steiner-quadrature space shift keying (QSSK) (S-QSSK), an innovative modulation scheme tailored for multiple–input multiple–output (MIMO) systems. The study compares S-QSSK with existing modulation schemes (S-space shift keying (SSK)(S-SSK), QSSK, generalized quadrature space shift keying (GQSSK)) across various performance metrics, considering diverse channel conditions, transmit antenna numbers, and spectral efficiencies. Theoretical average bit error rate (ABER) analysis, validated by Monte Carlo simulations, establishes a close agreement between analytical and practical results, especially at realistic signal-to-noise-ratio (SNR) values. Additionally, capacity and mutual information formulas shed light on the system’s information transmission capabilities. The research delves into the outage probability, power consumption, and computational complexity of S-QSSK MIMO systems. It unveils a promising trade-off, demonstrating that S-QSSK achieves competitive performance in ABER, capacity, and mutual information, positioning it as a practical modulation technique. Importantly, S-QSSK exhibits about a 1 dB ABER performance decrease compared to S-SSK and QSSK, while offering a noteworthy 2 dB improvement over GQSSK. Despite requiring slightly more transmit antennas than GQSSK, S-QSSK demands significantly fewer than both S-SSK and traditional QSSK, particularly when targeting high spectral efficiencies. Moreover, the analysis underscores the advantageous power consumption and favorable computational complexity of S-QSSK relative to other modulation schemes. The proposed S-QSSK MIMO system thus presents itself as an efficient solution, enabling high data rates with fewer transmit antennas, resulting in substantial reductions in power consumption and computational complexity without a significant sacrifice in ABER and mutual information. These attributes make it a compelling choice for various applications, including those requiring efficient massive multiple–input multiple–output (mMIMO) operations.

Mathematical modeling of minimization of electricity consumption by industrial enterprises with continuous production

Relevance. Determined by the importance of minimizing electrical power consumption in industrial enterprises with continuous production, considering the specific characteristics of their technological processes and the requirements to maintain the output volume of their products. Aim. To solve the task of minimizing electrical power consumption based on a mathematical model and gradient method under optimal planning of the production volume of the industrial enterprises with continuous production; to develop a mathematical model for optimal distribution of production over a time cycle (month, quarter, year) across departments, taking into account both simple and functional constraints, derived from the condition of ensuring minimal electrical power consumption in industrial enterprises with continuous production. Methods. When developing the mathematical model for ensuring minimal electrical power consumption while preserving the production volume, classic Lagrange optimization methods were used. To ensure sufficient calculation accuracy, iterative methods were also applied. For the task under consideration, a calculation error margin of ε=0,1 was assumed and established. It is known that the choice of calculation error margin depends on the specifics of the problem at hand and the decision-maker. To verify the adequacy of the developed model, the method of finding the relative extremum of a function of several variables was used. Results. The use of the mathematical model, which takes into account the nature of the technological process and boundary conditions in both simple and integral forms, demonstrated the feasibility of optimal planning of electrical power consumption by the enterprise. The effectiveness of the developed approaches was verified using a metallurgical enterprise as an example of an industrial enterprise with continuous production, in solving the task of minimizing electrical power consumption for products produced during the reporting period. The use of the proposed model allowed for a reduction in annual electrical power consumption by 2.5% while maintaining the same production volume. One of the classic optimization methods – the method of finding the relative extremum of functions of several variables – showed almost identical results upon verification. This serves as further evidence of the adequacy of the proposed model.

Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads

This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and Hf0.5Zr0.5O2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Hf}_{0.5}\ ext {Zr}_{0.5}\ ext {O}_2$$\\end{document} (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.

Design of novel low cost triple-node-upset self-recoverable hardened latch

CMOS devices are increasingly affected by triple-node-upset as transistor characteristics reduce, particularly in radiation environments. For the shortcomings of the existing radiation hardened designs, including high overhead and high delay, this paper proposes a novel low cost triple-node-upset self-recoverable latch. Simulation results show that compared with the existing triple-node-upset hardened designs, the proposed latch has reduced power consumption, delay, and power-delay product by 34.57 %, 6.42 %, and 34.98 %, respectively.

Enhancement of spin–orbit torque and magnetization switching by Pt100–xWx alloy in Co-based films

Alloying heavy metals (HMs) has been an effective method for enhancing the efficiency of spin–orbit torque. In this study, we demonstrate that Pt100−xWx/Pt/Co/Ta multilayers still maintain perpendicular magnetic anisotropy after high-temperature annealing. Doping tungsten (W) into HM platinum (Pt) at the bottom of a Pt/Co/Ta multilayer significantly increases the spin Hall angle (θSH) and reduces the critical switching current density (Jc). The harmonic Hall test results show that the θSH of the Pt88W12 alloy film is approximately 0.29, which is higher than that of the pure Pt film (0.15). The magnetization switching test reveals that the Jc of Pt94W6 alloy film is approximately 4.892 × 106 A/cm2, which is 51.9% lower than that of the pure Pt film. This study offers a valuable method for reducing power consumption and enhancing the efficiency of related application devices.

High performance electrically-derived single-pixel magnetophotonic spatial light modulator

Spatial light modulators (SLMs) utilize components such as magnetophotonic crystals (MPCs) to alter specific characteristics of a light beam in space. In magneto-optical (MO) spatial light modulators, MPCs play a crucial role in spatially modulating light by manipulating local light intensity and polarization rotation. This study explores how the refractive indices of birefringent electro-optical materials within MPCs influence MO rotation and reflection. In this order, using a transfer matrix method, we designed thin MPCs with a MO rotation around 90° and almost full reflection, for having SLMs with high contrast. The direction of applied voltage to the structure was found to be a critical factor, leading to significant reduction in power consumption for SLMs incorporating the MPCs. These characteristics render the designed MO spatial light modulator well-suited for applications in projectors and displays.

Fuzzy control allocation of a positionable rotor quadrotor based on log‐barrier optimization and propulsion system fault toleration

AbstractHerein, a control system and a fault tolerance method for the rotor positionable quadrotor are proposed. Quadrotors that have a variable structure are made for different purposes. The rotor‐positionable quadrotor studied here, is a type of drone with a variable structure that has the ability to change the position of its rotors linearly along the axis of each arm. It can be seen that this capability can improve the drone robustness against disturbances and faults in comparison with regular quadcopters. Due to the over‐actuated dynamics of this type of quadrotor, the control allocation scheme based on log‐barrier optimization is employed to obtain the position and speed of each rotor. In this study, it is experimentally shown that rotor positioning not only reduces power consumption but also increases roll and pitch control inputs magnitude. Furthermore, when a fault occurs as a decrease in rotor speed, a fuzzy method is proposed to position the rotors which tolerates the fault. Finally, numerical simulations and experimental tests verified that rotor positioning can bring more robustness, reduction in power consumption, and fault tolerance in some rotor faults capabilities for quadrotors.

Multi-scale full spike pattern for semantic segmentation

Spiking neural networks (SNNs), as the brain-inspired neural networks, encode information in spatio-temporal dynamics. They have the potential to serve as low-power alternatives to artificial neural networks (ANNs) due to their sparse and event-driven nature. However, existing SNN-based models for pixel-level semantic segmentation tasks suffer from poor performance and high memory overhead, failing to fully exploit the computational effectiveness and efficiency of SNNs. To address these challenges, we propose the multi-scale and full spike segmentation network (MFS-Seg), which is based on the deep direct trained SNN and represents the first attempt to train a deep SNN with surrogate gradients for semantic segmentation. Specifically, we design an efficient fully-spike residual block (EFS-Res) to alleviate representation issues caused by spiking noise on different channels. EFS-Res utilizes depthwise separable convolution to improve the distributions of spiking feature maps. The visualization shows that our model can effectively extract the edge features of segmented objects. Furthermore, it can significantly reduce the memory overhead and energy consumption of the network. In addition, we theoretically analyze and prove that EFS-Res can avoid the degradation problem based on block dynamical isometry theory. Experimental results on the Camvid dataset, the DDD17 dataset, and the DSEC-Semantic dataset show that our model achieves comparable performance to the mainstream UNet network with up to 31× fewer parameters, while significantly reducing power consumption by over 13×. Overall, our MFS-Seg model demonstrates promising results in terms of performance, memory efficiency, and energy consumption, showcasing the potential of deep SNNs for semantic segmentation tasks. Our code is available in https://github.com/BICLab/MFS-Seg.

Compact polarization transceiver module for fiber optic gyroscopes

This paper reports a transceiver module with a high polarization extinction ratio (PER) for the polarization-maintaining interferometric fiber optic gyroscope (PM-IFOG). This transceiver utilizes a graded-index lens featuring a slanted face coated with a multilayer film, enabling simultaneous collimation and beam splitting. Configurations of the graded-index splitter lens and the corresponding transceiver module are simulated and designed using the ray matrix method and physical optical propagation. The optical loss of the transceiver module is about 3.77 dB, and the PER exceeds 25 dB. The IFOG prototype is implemented by integrating the transceiver module with a PM sensing coil. The bias instability of the IFOG prototype is evaluated to be 0.51°/h by Allan variance. This research contributes to the reduction in size, weight, power consumption, and cost of PM-IFOG systems.

Simulated Channel Length Variation Effects on Regulated Cascode Input Stage-Based Transimpedance Amplifier for Fiber Optics Applications

A proposed transimpedance amplifier with channel length variation is simulated. The amplifier consists of a regulated cascode input stage followed by a common gate-common source configuration. A channel length series (45 nm, 90 nm and 130 nm) in CMOS technology was introduced within the proposed amplifier in order to achieve comparative performance analysis. There are two key findings from this study. On one hand, it was found that the trade off in gain versus bandwidth and input referred noise current still applies when channel length is moved upward from 45 nm up to 130 nm. A series of transimpedance amplifier gains (42.16 dBΩ, 44.34 dBΩ and 46.25 dBΩ) that correspond to (1.80 GHz, 1.33 GHz and 1.06 GHz) of bandwidths is reported corresponding to the above channel length series respectively with an input referred noise current spectral density series (16.35 pA/sqrt(Hz), 12.17 pA/sqrt(Hz) and 10.60 pA/sqrt(Hz)) of reduction. On the other hand, a reduction in power consumption occurred as channel length is moved upward for the same proposed topology. A total power consumption series (0.611 mW, 0.287 mW and 0.173 mW) was reported that corresponds to the above channel length series.

A cutting tool architecture designed to address the parasitic mechanisms consuming excess power during machining and manufacturing operations-A review-based study towards sustainable manufacturing.

Metal cutting has been extensively studied over the years for improving its efficacy, yet, parasitic mechanisms like chatter and tool wear continue to generate higher forces and energy consumption with poor surface integrity. To address these parasitic mechanisms, a single-point turning cutter design is proposed based on the physics-of-machining including chatter theory to achieve reduced power consumption during the cutting of various metallic alloys like Al-6061, Ti-6Al-4V and others used by critical sectors such as aerospace and automotive. The current work focuses on aspects of machining that effectively reduce parasitic forces feeding into cutting power. The proposed cutter amalgamates features such as optimum side and end cutting edge angles, smaller nose radius and textured rake face into the cutter-body. Such a design is further proposed for use with a mechanochemical effect on a recently discovered plastic flow mode called sinuous flow, which has been reported to bring down cutting forces significantly. Experimental and analytical tests on the cutter design features validate reduction of cutting forces and through that alleviate the tendency to chatter as well as bring about energy savings for cutting of Al 6061. The potential for reduced real-time power consumption makes this design-framework significant for multipoint milling cutters too. It will greatly facilitate frugal manufacturing to account for sustainability in manufacturing operations.

An efficient beaconing of bluetooth low energy by decision making algorithm

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it’s especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments.

Ultra-efficient and thermally-controlled atmospheric structure deicing strategy based on the Peltier effect

Snow and ice coverage pose significant threats to the safe and stable operation of energy equipment. To address the challenges of high power consumption and thermal damage to materials resulting from prolonged use of thermal ice melting technology, this study developed and tested a low-power, thermally controlled de-icing system. The system employs thermoelectric material as the heating element to minimize power consumption and prevent thermal damage to the substrate material. Compared to traditional thermal elements, it offers advantages such as single-sided heat production, a high thermal performance coefficient, and a rapid temperature rise rate. Investigating the effects of temperature difference and current on the heat production coefficient of the system and conducts comparative experiments on the thermal response of different heating elements in low-temperature environments. Furthermore, to further reduce power consumption and minimize unnecessary sensible heat within the ice layer, the thermal control system is designed to maintain the interface temperature between 3 and 5 ℃. This approach selectively melts the interface ice layer to form a water film, facilitating ice layer removal through gravity and centrifugal force. The de-icing energy density achieved with this method is only 10.31 J/cm2, resulting in a 32.167 % reduction in power consumption compared to continuous heating. For the flat plate heating structure, the study explores the physical mechanism of interface temperature control by establishing the relationship between temperature and current through a thermal circuit model. The calculated results closely align with experimental findings, with the maximum error not exceeding 15 %.

Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control

Due to the extensive implementation of the fifth generation wireless communication networks (5 G), numerous base stations are being strategically deployed in densely inhabited areas to address the escalating need for network traffic. Within wireless cellular networks, over 80% of energy consumption is primarily concentrated on the side of the base station. In comparison to 4 G base stations, the 5 G base stations exhibit notable enhancements in terms of bandwidth, peak rate, and transmission power. Nonetheless, they suffer from limited signal coverage and extensive resource consumption. To address this challenge effectively, the present study introduces a Mobile Perception Dual-Level (MPDL) base station sleep control strategy based on cellular traffic forecasting. The strategy aims to satisfy the demands for reduced power consumption and enhanced service quality by monitoring both the current network traffic of the base station and the user's mobility, consequently placing the base station into two different sleep states. Specifically, we first designed an inverted Transformer (iTransformer) model to accurately forecast cellular traffic. The model is capable of effectively capturing both the spatial and temporal attributes of cellular traffic. It also takes into account the impact of geographical features, such as Points of Interest (POIs) and the quantity of Base Stations (BSs), on the forecasting outcomes. Consequently, it attains precise predictions of the cellular traffic. Based on the forecasting outcomes of cellular traffic, this study presents a mobility perception dual-level sleep strategy. This strategy proposal differentiates sleep into deep sleep and light sleep, while also taking into account the mobility attributes of users. It incorporates considerations for Quality of Service (QoS) and energy consumption. We assess the performance and application impacts of cellular traffic forecasting and base station sleep strategy in real-world datasets. Several experiments and validations have confirmed that the MPDL, as proposed in this paper, effectively decreases power consumption by a range of 22% to 41% when compared to baselines.

The Future of Computing: Microprocessor Advancements in 2024

In 2024, the microprocessor landscape witnessed a revolutionary shift, marked by cutting-edge advancements and transformative trends across various industries. The relentless pursuit of Moore's Law drove innovation, leading to the creation of highly efficient processors through state-of-the-art fabrication technologies. Embracing sub-3nm processes, semiconductor manufacturers enhanced performance metrics while reducing power consumption. The rise of heterogeneous computing architectures and specialized edge processors tailored for decentralized environments further diversified the microprocessor ecosystem, setting the stage for unprecedented levels of performance, efficiency, and adaptability. These developments not only shaped the future of computing but also underscored the pivotal role of microprocessors in driving digital innovation Keywords Artificial Intelligence (AI), Sports Bikes, Ride Quality, User Experience, Safety, Adaptive Cruise Control, Collision Avoidance Systems, Rider Safety, Engine Performance Improvements, Predictive Maintenance Algorithms, Bike Performance, Dependability, AI Driven Design Techniques Keywords: Microprocessor, Fabrication technologies, Sub-3nm processes, Microprocessor ecosystem.

A 12-Bit 1.2-GS/s Current-Steering DAC in 45-NM CMOS Technology

This research paper presents a 12-bit 8–4-segmented current-steering DAC (digital-to-analog converter) that offers notable advantages, particularly in terms of its reduced power consumption compared to other similar designs. The exceptional performance of our DAC can be attributed to two key factors: the use of a thermometer encoder and an efficient digital input bit segmentation. These features contribute to enhanced precision and reduced power requirements, setting our DAC apart from the existing solutions in the field. These enhancements significantly reduce glitches and improve the accuracy of the DAC’s output. DAC dissipates a total power of about 44.95[Formula: see text]mW when running at a sample rate of 1.2[Formula: see text]GHz with the CMOS technology of 45[Formula: see text]nm. While the supply input voltage is held constant at 1.2[Formula: see text]V, the DAC’s digital input is pulsating in nature in which the ON voltage is 1[Formula: see text]V and OFF voltage is 0[Formula: see text]V. Also, the DAC has a spurious-free dynamic range (SFDR) of 41.77[Formula: see text]dB.

Modification of impeller blades for the efficient mixing of particulate suspensions in a stirred tank

The fractal arrangement of perforated holes were introduced in the impeller blades for the suspension of solid particles in a stirred tank. The mixing characteristics of solid-liquid two phases in a stirred tank with a fractal arrangement perforated impeller (FAP impeller) were investigated. Results indicated that incorporating fractal arranged perforated holes in the impeller blades resulted in a reduction in power consumption (P) compared to conventional designs under the same agitation speed. Moreover, a rise in the iteration quantity of fractal arranged perforated holes in impeller blades resulted in higher power consumption and power number, but still lower than those observed with conventional designs. FAP impeller enhanced turbulent intensity, increased shear effect on the fluid, minimized trailing vortex dimension behind the impeller blades, and promoted the axial circulation of solid particles, ultimately leading to the better suspension quality of solid particles compared to pitched blade impeller at the constant P. The above phenomena became increasingly evident as the iteration number in the fractal arranged perforated holes in the FAP impeller blades increased.

Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals

Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs.

Introduction to the ONDM 2023 special issue

This JOCN special issue contains extended versions of selected papers presented at the 27th International Conference on Optical Network Design and Modeling (ONDM 2023), which took place on 8–11 May 2023 at the University of Coimbra, Coimbra, Portugal. The articles in this special issue contain several current topics of optical networking research: quality of transmission (QoT) estimation and its importance in network control and optimization, strategies to reduce power consumption in optical networks, analysis of optical network resilience from the link level up to inter-carrier networks, and strategies to upgrade long-haul quantum key distribution networks.