Power Reduction Research Articles

Partitioning for Enhanced Statistical Power and Noise Reduction: Comparing One-Way and Repeated Measures Analysis of Variance (ANOVA)

Partitioning for Enhanced Statistical Power and Noise Reduction: Comparing One-Way and Repeated Measures Analysis of Variance (ANOVA)

3D simulations of the RF wave propagation and power coupling in cold magnetized plasma near an ICRF antenna

Abstract The electromagnetic wave propagations and their coupling characteristics in magnetized plasma near the antenna of Ion Cyclotron Range of Frequencies (ICRF) is studied based on self-developed 3DFEM-IA code. This code effectively resolves the three-dimensional (3D) geometry and the electromagnetic field using the finite element method. Our findings reveal that the distributions of electromagnetic fields and energy flow density significantly depend on the antenna phases, surface current density on the antenna straps, and background plasma density. Notably, the non-uniform surface current density on the antenna straps, resulting from the presence of induced currents, contributes to a reduction in coupling power within the edge plasma. Furthermore, the calculated coupling impedance increases with plasma density, corroborating well with experimental measurements.

Theta power reduction and theta–gamma coupling desynchronization are associated with working memory interference and anxiety symptoms in panic disorder: a retrospective study

BackgroundTheta-gamma coupling (TGC) describes the modulation of gamma oscillations by the theta phasic activity, which is crucial for processes such as the ordering of information during working memory (WM) performance. The mental arithmetic (MA), which involves performing calculations with numbers, is a crucial tool for evaluating and understanding the sensory processing and management abilities of WM. Evaluating TGC may provide greater insight into the neural mechanisms mediating WM deficits in panic disorder (PD).MethodsMedical and electroencephalography (EEG) records of psychiatric outpatient clinic between 1 March 2020 and 30 September 2023 were retrospectively reviewed. A total of 34 PD patients and 34 age- and sex-matched healthy controls (HCs) underwent EEG to assess the overall functional interaction of the brain using multi-channel EEG analysis, focusing on specific brain regions including the frontal, temporal, parietal, and occipital lobes. EEG recordings were conducted during two sessions: a 5-min eyes-closed resting-state (RS) and a subsequent 5-min eyes-closed MA. The TGC and the spectral power of the theta and gamma frequency bands, which are well known to be associated with WM, were analysed.ResultsCompared to those in HCs, TGC and theta power were significantly attenuated in PD patients. When analysing both HCs and PD patients together, RS TGC and relative theta power were negatively correlated with state anxiety and perceived stress scores, respectively. In contrast, TGC and relative theta power during the MA condition were positively correlated with the MA performance. Specifically, in PD patients, RS theta power across all electrodes was significantly negatively correlated with the Hamilton Anxiety Scale (HAMA) score. Linear regression analysis revealed that theta power in the T5 channel remained negatively correlated with pathological anxiety as measured by the HAMA score, even after controlling for other confounding factors.ConclusionsThis study highlights significant alterations in TGC and theta power in PD patients. PD patients exhibit reduced TGC and theta power compared to HCs, indicating deficits in the neural mechanisms underlying anxiety and/or WM in PD. These insights contribute to a better understanding of the neural basis of WM deficits in PD and suggest potential avenues for targeted therapeutic interventions.

The future of offshore wind power production: Wake and climate impacts

Rapid deployment of offshore wind is expected within the coming decades to help meet climate goals. With offshore wind turbine lifetimes of 25–30 years, and new offshore leases spanning 60 years, it is vital to consider long-term changes in potential wind power resource at the farm planning stage. Such changes may arise from multiple sources, including climate change, and increasing wake-induced power losses. In this work, we investigate and compare these two sources of long-term change in wind power, for a case study consisting of 21 wind farms within the German Bight. Consistent with previous studies, we find a small but significant reduction in wind resource due to climate change by the end of the 21st century under the high-emission RCP8.5 scenario, compared with a historical period, with a mean power reduction (over an ensemble of seven climate models) of 2.1%. To assess the impact of wake-induced losses due to increasingly dense farm build-out, we model wakes within the German Bight region using an engineering wake model, under various stages of (planned) build-out corresponding to the years 2010–2027. By identifying clusters of wind farms, we decompose wake effects into long-range (inter-cluster), medium-range (intra-cluster) and short-range (intra-farm) effects. Inter-cluster wake-induced losses increase from 0 for the 2010 scenario to 2.5% for the 2027 scenario, with intra-cluster losses also increasing from 0 to 4.3%. Intra-farm losses are relatively constant, at around 13%. While the evolution of wake effects therefore outweighs the climate effect, and impacts over a shorter timescale, both factors are significant. We also find evidence of an interaction between the climate and wake effects. Both climate change and evolving wake effects must therefore be considered within resource assessment and wind farm planning.

Theta and gamma modulation in the nucleus accumbens as drivers of neurophysiological responses to acute methamphetamine sensitization in mice.

Methamphetamine (METH) has well-documented long-term effects on the brain, including increased psychomotor activity and behavioral sensitization. However, its immediate effects on the brain's reward system following acute exposure, which may contribute to the development of addiction, are less understood. This study aimed to investigate the effects of acute METH on brain oscillations in the nucleus accumbens of C57BL/6 mice. Mice in the METH group received 5mg/kg of METH for 5 days during the conditioning phase, followed by an 8-day abstinence period. Afterward, they underwent a 6-minute tail suspension test and were given a 1mg/kg METH challenge. Local field potential (LFP) data were analyzed for percent total power, mean frequency indices, and phase-amplitude coupling (PAC) to assess the neural effects of METH exposure across these phases. A reduction in theta power was observed across the conditioning, abstinence, and challenge phases of METH exposure. The subsequent METH challenge enhanced gamma oscillations, and PAC analysis revealed a consistent theta-gamma coupling index during both the METH administration and challenge phases. It highlights the sensitivity of the reward system to intense, short-term drug exposure, providing new insights into how acute neural stimulation may contribute to the development of addictive behaviors, reinforcing the brain's vulnerability to drug-induced changes in neural circuitry.

Integrating Cold Thermal Energy Storage for Air Conditioning Demand in a CO2 Refrigeration System at a Supermarket

A common configuration for transcritical CO2 booster systems in supermarkets involves air conditioning (AC) supplied by cooling a water-glycol circuit. The design capacity of the refrigeration unit must handle all refrigeration loads and the AC load during the hottest summer day, leading to overcapacity and part-load operation for most of the year. A proposed design for implementing cold thermal energy storage (CTES) dedicated to AC demand in a supermarket located in the Oslo region is modeled in the object-oriented language Modelica. Simulation results demonstrate an electricity peak power reduction of up to 32.33%. Even though energy savings are not the primary objective of this project, they are achieved by producing and storing energy when the outdoor temperature is lower, and the coefficient of performance (COP) of the system is higher. The energy savings can reach up to 11.8%. Finally, the economic benefits of the system are assessed under the spot pricing system, revealing potential electricity cost savings of up to 12.56%.

A saliency-aware analog computing in-memory with SAR-embedded detection achieving 18.5% power reduction

A saliency-aware analog computing in-memory with SAR-embedded detection achieving 18.5% power reduction

Low-Power Design Methodologies for Asynchronous FIFO with Integrated Power Reduction Techniques

Low-Power Design Methodologies for Asynchronous FIFO with Integrated Power Reduction Techniques

Effects of longitudinal bending stiffness and midsole foam on running energetics

Advanced footwear technology has become commonplace in the competitive running world. However, a systematic exploration of the effects of each of the primary components has not yet been presented. Our goal was to quantify running economy and step parameters in four different shoe conditions: PEBA shoes with plate, PEBA shoes without plate, EVA shoes with plate and EVA shoes without plate. Participants ran at 14 km/h (n = 8 males) or 12 km/h (n = 6 females) on a treadmill. The shoe order was randomly assigned for the four shoe conditions, where the participants wore each shoe twice in a mirrored order. There was a significant reduction in metabolic power (1.0%) associated with the presence of a plate, as well as a similar reduction based on foam type (1.0%). However, there was no significant interaction between presence of plate and foam type. Adding a plate significantly reduced metabolic power for EVA by 1.3% but not for PEBA. PEBA foam significantly reduced metabolic power by 1.3% in shoes without a plate, but not for the shoes with a plate. Step frequency and contact time were similar between shoes and not correlated to running economy improvements. Starting from a baseline condition with traditional foam without a plate, either adding a plate or using PEBA foam improved running economy with a similar amount (1.3%). Adding the alternate second feature non-significantly improved running economy with an additional 0.6%. The benefit of both technologies combined (1.9%) was smaller than the sum of its parts (1.3% each).

Four Quadrant Operation of Induction Motor Using H-Bridge in Harsh and Noxious Environment Field with Dust, Voltage Fluctuation and Major Load Variations

The investigation and findings show that the H Bridge variable frequency drive is the optimum choice for resolving intrinsic motor problems and energy conservation. Variable frequency drives outperform other strategies like soft starters because they offer numerous benefits over other techniques, including greater versatility and control over the motor's starting and stopping capabilities. Dynamic torque control, overload protection, and power reduction during idle are other essential aspects of variable frequency drives. Conserving energy to the greatest extent possible is one strategy to avoid energy shortages. It has been reported that 75% of the electrical energy generated globally is used by motors. It has been noted that the main problem with motors is that they use a lot of current to start. As a result, high inrush current produces excessive heat, reducing the lifespan of electrical equipment and using more electricity. Reducing the initial inrush current to a manageable level is advised. By using an induction motor and the H Bridge, this current can be decreased. H Bridge uses a technique known as variable speed drive, which is often referred to as variable torque drive, to control the speed and torque of AC induction motors. Pulse width modulation is used to monitor the motor's voltage and torque in this occurrence. Although there are several methods to reduce the motor's current, such as by employing soft starts, variable torque drives are more advantageous. The strong starting torque and flexible speed control of DC motors make them useful in a range of applications. However, because of the cost and commutation, there are a few significant problems. As a result, the PWM-powered H bridge inverter extends the life of machinery and provides electricity to electrical appliances in response to demand. The fact that it is an energy-saving gadget, however, is by far its greatest advantage. This is especially important in a country like India, where the energy crisis has halted economic growth.

GRMD: A Two-Stage Design Space Exploration Strategy for Customized RNN Accelerators

Recurrent neural networks (RNNs) have produced significant results in many fields, such as natural language processing and speech recognition. Owing to their computational complexity and sequence dependencies, RNNs need to be deployed on customized hardware accelerators to satisfy performance and energy-efficiency constraints. However, designing hardware accelerators for RNNs is challenged by the vast design space and the reliance on ineffective optimization. An efficient automated design space exploration (DSE) strategy that can balance conflicting objectives is wanted. To address the low efficiency and insufficient universality of the resource allocation process employed for hardware accelerators, we propose an automated two-stage design space exploration (DSE) strategy for customized RNN accelerators. The strategy combines a genetic algorithm (GA) and a reinforcement learning (RL) algorithm, and it utilizes symmetrical exploration and exploitation to find the optimal solutions. In the first stage, the area of the hardware accelerator is taken as the optimization objective, and the GA is used for partial exploration purposes to narrow the design space while maintaining diversity. Then, the latency and power of the hardware accelerator are taken as the optimization objectives, and the RL algorithm is used in the second stage to find the corresponding Pareto solutions. To verify the effectiveness of the developed strategy, it is compared with other algorithms. We use three different network models as benchmarks: a vanilla RNN, LSTM, and a GRU. The results demonstrate that the strategy proposed in this paper can provide better solutions and can achieve latency, power, and area reductions of 9.35%, 5.34%, and 11.95%, respectively. The HV of GRMD is reduced by averages of 6.33%, 6.32%, and 0.67%, and the runtime is reduced by averages of 18.11%, 14.94%, and 10.28%, respectively. Additionally, given different weights, it can make reasonable trade-offs between multiple objectives.

Divertor Tokamak Test: Impact of NBI shine-through and beam-plasma interaction on Divertor Tokamak Test facility

IntroductionIn this work, we aim to explore numerically the behavior of beam energetic particles in the Divertor Tokamak Test (DTT), a superconductive device equipped with a Neutral Beam Injection (NBI) system capable of injecting neutrals up to 510 keV.MethodWe explore beam ionization and beam slowing down for different DTT plasma scenarios. Numerical simulations are performed using the ASCOT suite of codes, including a wide-range scan of plasma density and beam injection energy. For different plasma conditions, we estimate shine-through losses, including the heat fluxes on the first wall thanks to dedicated particle tracing simulations. Orbits of newly-born fast ions are characterized by means of the constant of motion phase space, showing how trapped energetic particles’ population and prompt losses change with plasma density and NBI energy.Results and discussionSlowing down simulations show that NBI injection at 510 keV is well coupled to DTT plasmas. DTT NBI will be one of the sources of auxiliary ion heating, with an absorbed power ratio of up to ∼50% depending on plasma and beam parameters. At low plasma densities, energetic particle confinement is less efficient, and NBI power and/or energy reduction is expected.

PE-based high throughput and low power polar encoder for 5G-NR PBCH channel

In this paper, we propose a PE-based parallelism configurable polar encoder hardware architecture for emerging high-speed 5G communication system. This encoder architecture is applied to the 5G-NR PBCH channel polar encoder, implemented with functional modules such as CRC generation and interleaving, channel encoding, and rate matching as specified in the 3GPP protocol. Next, based on the united power format (UPF) low-power management technology, the PBCH polar encoder architecture is divided into different power domains based on their operating characteristics to reduce the power consumption. Experimental results show that the proposed polar encoder achieves throughput up to 30 Gbps. Based on TSMC 40 nm CMOS technology, by applying the proposed low-power methodology, the power consumption of the PBCH polar encoder at parallelisms of 8, 16, and 32 are 1.236 mW, 1.170 mW, and 1.084 mW, achieving power reductions of 24%, 29%, and 35% when comparing to non-low-power design, respectively.

Abstract Sa1107: Resuscitation of Lungs after Cardiac Arrest with Ex-Vivo Lung Perfusion: A Bench Study to Minimize Ventilator-Induced Lung Injury through Flow Modulation

Introduction: Lung donation after uncontrolled circulatory death (uDCD) following a witnessed cardiac arrest is feasible after resuscitation and assessment with ex vivo lung perfusion (EVLP). This bench study explores the efficacy of expiratory flow modulation in minimizing damage caused by mechanical power during volume-controlled ventilation within the EVLP procedure. Methods: Donor lungs were simulated using the ASL 5000 lung simulator (IngMar Medical) and ventilated in volume-controlled mode using the ViX-ventilator (KU Leuven), set at 7 breaths per minute with a positive end-expiratory pressure of 5 cmH 2 O. (Figure 1) Baseline measurements were obtained to deliver tidal volumes (VT) of 300, 400, and 500 mL. These measurements were then compared to an intervention where expiratory flow was modulated using an in-house designed device, the flow modulator. Mechanical power (J/min) was calculated using the pressure-volume curve. Differences in power between the baseline and various levels of flow modulation were assessed using a one-sided t-test. Results: No clinically relevant differences were observed in VT delivery between baseline settings and during expiratory flow modulation. (Table 1) However, significant differences in mechanical power were noted between baseline and flow modulation across the three VT settings (p<0.001). The reduction in power varied from 10.9% to 58.5%, depending on the selected VT and the degree of flow modulation applied. Notably, the extent of power reduction increased with the level of flow modulation. Conclusion: Flow modulation in the expiratory circuit during volume-controlled ventilation in a bench set-up does not affect VT delivery and can significantly reduce mechanical power by up to 58.5%. This reduction in mechanical power could potentially reduce iatrogenic damage during EVLP. Consequently, this might extend preservation times and improve the resuscitation outcomes of uncontrolled DCD lungs. Emergency medicine has the opportunity to leverage this vital pool of donor lungs, moving towards a future with minimal transplant waitlists.

Mid-infrared arrayed waveguide gratings using a quantum cascade laser gain medium as core material

Mid-infrared photonics is a widely researched field with several applications, such as chemical sensing and spectroscopy. The development of photonic integrated circuits for the mid-infrared can enable the reduction in device size, weight, and power (SWaP) consumption. This paper demonstrates arrayed waveguide gratings working in the mid-infrared regime (5–5.4 µm). Our devices are fabricated on an InP-based quantum cascade laser platform with the gain medium as the waveguide core. To minimize the propagation losses caused by free carrier absorption and intersubband absorption in the unbiased QCL structure, we exposed the photonic chips to proton implantation. The performance of three sets of AWGs with different etch depths was characterized. The lowest waveguide losses were measured to be 2 dB/cm. The best performing 7×1 AWG and 13×1 AWG designs featured insertion losses of −2dB and −2.5dB, respectively. This study showcases the feasibility of applying such a platform for easy integration with active components like lasers and photodetectors, paving the path for on-chip mid-infrared applications.

Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling

Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling

Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling

Stochastic sampling is performed to reduce hardware energy consumption and prevent overfitting by reducing parameters, because not all data are required for learning. In this study, a new approach, pseudo‐synaptic sampling (PS2) method, which approximates the conventional synaptic sampling machine (S2M) method through a hardware‐friendly implementation while demonstrating superior efficiency, is introduced. By sampling in front of neurons rather than at each synapse, the PS2 method improves hardware energy efficiency and ensures scalability. Furthermore, it improves energy and area efficiency by eliminating the additional circuit required by other techniques, such as the random walk (RW) method previously used which requires an additional circuit to frequently charge/discharge the membrane potential. Herein, the average firing rate equation for the S2M method is modified to suit the experimental conditions of this study. Through this numerical simulations, it is confirmed that the activation function of the PS2 method aligns with that of the S2M method and verified that the PS2 method can implement stochasticity for restricted Boltzmann machine (RBM) neurons. Experimental validation of the PS2 method, compared to the RW method, for Modified National Institute of Standards and Technology database (MNIST) training and inference on field‐programmable‐gate‐array‐implemented spiking RBM chips reveals promising results. In an MNIST 100‐handwritten digit experiment, the PS2 method exhibits on‐chip training accuracy (92%) comparable to that of the RW method (93%). Furthermore, in the energy consumption analysis, it is shown that the PS2 method reduces power consumption by 94.94% compared to the RW method, highlighting its enhanced power efficiency due to a reduced number of circuit elements. In the investigation into the impact of increasing the frequency at which random bits are generated, it is shown that the RW method experiences accuracy degradation even with slight increases, whereas the PS2 method maintains accuracy over significantly longer periods. This enables further power reduction by allowing for a longer period during random bit generation. In this study, a foundation is laid for maximizing the energy efficiency of spiking neural network processors by optimizing internal noise generation mechanisms.

Control of Large Wind Energy Converters for Aeroacoustic Noise Mitigation with Minimal Power Reduction

The population is often opposed to wind turbines being erected near their homes, mainly because the machines are noisy, especially at night. In an effort to establish a compromise between the needs of the people and the fulfilment of energy demands, wind turbines have the ability to switch between day and night operation by reducing the rotation speed during the night, resulting in a loss of generated power. The present study investigates simple models for noise emission, propagation, and prediction, with the objective of proposing a control system configuration that continuously adjusts the rotational speed as much as necessary until it matches sound level regulations while minimising power losses. Thus, several approaches are implemented and tested with a very large reference wind turbine. The simulation results of a reference wind turbine show that the approaches provide significant improvements in sound reduction as well as in power conversion.

Energy-efficient design and CNFET implementation of GDI-based ternary prefix adders

Abstract Ternary adders have produced more benefits compared to binary adders i.e., the ternary adder occupies less amount of area as well as produces less interconnect complexity. However, the CMOS implementation of the ternary adders failed to perform the process when the channel length was taken as 32 nm. At 32 nm technology, the CMOS transistors exhibit undesired effects such as Short Channel Effects (SCEs), mobility degradation, high leakage current, etc. Multi-gate devices are preferred to overcome these issues. Carbon Nano-tube Field Effect Transistors (CNFETs) are one of the technologies to work efficiently when the channel length is 32 nm. In this paper, CNFET-based ternary prefix adders are designed. Power consumption is the most critical requirement for the VLSI system, as it enhances energy efficiency and reduces heat dissipation. One way to achieve this power reduction is by minimizing the number of transistors employed in the adder circuits. This study employed a reduction technique known as Gate Diffusion Input (GDI) logic included in the proposed prefix adder design. The overall experimental investigation is done with the help of the HSPICE supporting platform. The proposed adder improved by reducing the power by up to 83%, energy by up to 83%, current by up to 78%, and delay by up to 96%. Finally, the Power Delay product (PDP) was also reduced by 84% compared to existing ternary adders. The proposed design proves to be highly effective in implementing the neuron structure, with the corresponding parameters thoroughly analysed and well-documented in this study.

An efficient leakage power optimization framework based on reinforcement learning with graph neural network

Threshold voltage (Vth\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{th}$$\\end{document}) assignment is convenient for leakage optimization due to the exponential relation between leakage power and Vth\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{th}$$\\end{document} by swapping logic cells without routing effort. However, it poses great challenge in large scale circuit design as an NP-hard problem. Machine learning-based approaches have been proposed to solve this problem, aiming to achieve well tradeoff between leakage power reduction and runtime speed up without new induced timing violation. In this paper, a leakage power optimization framework based on reinforcement learning (RL) with graph neural network (GNN) is first-ever proposed to formulate Vth\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{th}$$\\end{document} assignment as a RL process by learning timing and physical characteristics of each circuit instance with GNN. Multiple instances are selected in a non-overlapped manner for each RL action iteration to speed up convergence and decouple timing interdependence along circuit path, where the corresponding reward is carefully defined to tradeoff between leakage reduction and potential timing violation. The proposed framework was validated by the Opencores and IWLS 2005 benchmark circuits with TSMC 28 nm technology. Experimental results demonstrate that our work outperforms prior non-analytical and GNN-based methods with better leakage power optimization by additional 5% to 17% reduction, which is highly consistent with the commercial tool. When transferring the trained RL-based framework to unseen circuits, it achieves the roughly identical leakage optimization results as seen circuit and speed up the runtime by 5.7×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} to 8.5×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} compared with commercial tool.