Power Consumption Research Articles

Inhibiting the current spikes within the channel layer of LiCoO2-based three-terminal synaptic transistors

Synaptic transistors, which emulate the behavior of biological synapses, play a vital role in information processing and storage in neuromorphic systems. However, the occurrence of excessive current spikes during the updating of synaptic weight poses challenges to the stability, accuracy, and power consumption of synaptic transistors. In this work, we experimentally investigate the main factors for the generation of current spikes in the three-terminal synaptic transistors that use LiCoO2 (LCO), a mixed ionic-electronic conductor, as the channel layer. Kelvin probe force microscopy and impedance testing results reveal that ion migration and adsorption at the drain–source-channel interface cause the current spikes that compromise the device's performance. By controlling the crystal orientation of the LCO channel layer to impede the in-plane migration of lithium ions, we show that the LCO channel layer with the (104) preferred orientation can effectively suppress both the peak current and power consumption in the synaptic transistors. Our study provides a unique insight into controlling the crystallographic orientation for the design of high-speed, high-robustness, and low-power consumption nano-memristor devices.

Federated learning for millimeter-wave spectrum in 6G networks: applications, challenges, way forward and open research issues

The emergence of 6G networks promises ultra-high data rates and unprecedented connectivity. However, the effective utilization of the millimeter-wave (mmWave) as a critical enabler of foreseen potential in 6G, poses significant challenges due to its unique propagation characteristics and security concerns. Deep learning (DL)/machine learning (ML) based approaches emerged as potential solutions; however, DL/ML contains centralization and data privacy issues. Therefore, federated learning (FL), an innovative decentralized DL/ML paradigm, offers a promising avenue to tackle these challenges by enabling collaborative model training across distributed devices while preserving data privacy. After a comprehensive exploration of FL enabled 6G networks, this review identifies the specific applications of mmWave communications in the context of FL enabled 6G networks. Thereby, this article discusses particular challenges faced in the adaption of FL enabled mmWave communication in 6G; including bandwidth consumption, power consumption and synchronization requirements. In view of the identified challenges, this study proposed a way forward called Federated Energy-Aware Dynamic Synchronization with Bandwidth-Optimization (FEADSBO). Moreover, this review highlights pertinent open research issues by synthesizing current advancements and research efforts. Through this review, we provide a roadmap to harness the synergies between FL and mmWave, offering insights to reshape the landscape of 6G networks.

Effects of Pressure on Nitrogen Content and Solidification Structure during Pressurized Electroslag Remelting Process with Composite Electrode

The nitrogen content and solidification structure of the 1Mn18Cr18N ingots produced by the customized laboratory‐scale vacuum induction melting furnace and the pressure electroslag remelting furnace (PESR) with novel composite electrode under different pressure and the same power consumption are compared and studied. The results show that there are perfectly uniform radial chromium and nitrogen profiles during the PESR process. The nitrogen uptake reaction in the PESR process with composite electrode takes place on the liquid metal film on the electrode. Nitrogen uptake could be improved by increasing the nitrogen partial pressure. In addition, the basin depth at a pressure of 0.1 and 1.22 MPa is about 41 and 38 mm, the angle of the grains with respect to the vertical axis is 35° and 31°, respectively. The flat metal basin profile resulted from the thermal resistance at the slag–mold interface decreasing with increasing pressure. Primary and secondary dendritic arm spacing (PDAS and SDAS) variations exhibit an increasing and subsequently decreasing the trend as they move further away from the center in a horizontal direction. Both PDAS and SDAS decrease with increasing pressure from 0.1 to 1.22 MPa.

Chiral Perovskite Nanowire Optoelectronic Synapse for Full‐Stokes Polarization‐Resolved Perception and Reservoir Computing

AbstractFull‐Stokes polarization‐resolved photonic artificial synapse (PAS) devices with in‐sensor computing capabilities show great potential for multimode vision perception and processing. However, current polarization‐resolved PAS devices are limited to monotonic recognition of linearly polarized light or circularly polarized light. Here, the study develops a chiral perovskite nanowire‐based PAS device as a full‐Stokes polarimeter to achieve polarization‐dependent neuromorphic optoelectronics. The device demonstrates a significantly high circular dichroism of over 400 millidegree and intrinsic linear dichroism, contributing to effectively sensing polarized light. Efficient charge separation at the perovskite NW/MXene heterostructure results in a prolonged carrier lifetime, thereby achieving a high responsivity of 2.3 AW−1, excellent synaptic behavior, and low power consumption of 0.5 pJ per synaptic stimuli. Significantly, the polarization‐modulated reservoir computing is developed based on the full‐Stokes PAS device, which exhibits a low normalized root mean square error of 0.023 in the chaotic system forecasting task. The single‐nanowire PAS device with the capability of sensing full‐Stokes parameters demonstrates great significance in building advanced energy‐efficient polarization‐resolved nanoscale neuromorphic vision systems.

Design and test on high-gap wheeled agricultural chassis for harvesting broccoli

Abstract According to the planting mode of broccoli and the growth characteristics of broccoli, a kind of high-gap agricultural chassis with structural reliability and strong stability was designed for broccoli picking. Firstly, the dynamics and kinematics of the chassis were analyzed, and then we analyzed the stability and completed the finite element analysis of the key components. Finally, the simulation and field test were carried out. Thus, the main performance parameters of the chassis are determined. The overall power, driving power consumption, maximum endurance time, maximum driving distance, maximum power storage, and maximum battery power were 5.959kw, 1.84kw, 3h, 21km, 5.63kwh, and 6.552kw respectively. The results of the slope climbing and ridge driving test were better than those of the simulation test. The actual slope climbing effect of the chassis accords with the calculation of theoretical value, and it can climb the soil slope of 25°. These findings proved the stability of the high-gap agricultural chassis in the field and indicated that the chassis can be applied to small fields in hilly and mountainous regions.

Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry Enterprises

Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts’ confidence in the results of the information system.

Performance analysis of meta-heuristic optimization techniques for multi-objective VLSI circuit partitioning

Abstract The efficiency of various stages of physical design in the Very Large Scale Integration (VLSI) circuit can be enhanced by using multi-objective optimization techniques. The circuit is partitioned into a set of modules at the beginning of the physical design phase. This step is known as circuit partitioning. All subsequent physical design stages, like floor planning, placement, pin configuration, and routing etc, are influenced by the outcome of the circuit partitioning method being used. The efficiency of the partitioning method controls the quality of ultimate architecture. The efficiency achieved by the final product may be decreased by incorrect partitioning. This study compares the effectiveness of four Meta-heuristic optimization methods include the Genetic Algorithm (GA), Satin Bowered Bird (SBO), Particle Swarm Optimization (PSO), Simulated Annealing (SA) — in terms of number of net cut, duration of sleeping mode, and efficiency in power consumption for addressing the VLSI circuit partitioning problem. This experiment achieves K-way partitioning of the circuit that minimizes net cuts as well as maximizes sleep duration simultaneously, by evaluating optimization function. Solutions developed using GA, SBO and PSO consistently surpass those acquired using SA with respect to quality of optimal solution. The average optimum net cut and sleep duration achieved by this experiment are 10.7 and 9.9 in GA ; 11.5 and 12.3 in SBO, 11.80 and 9.10 in PSO; and 16.8 and 6.7 in SA based method respectively. This research resulted in a considerable reduction in power consumption, with the system’s average power efficiency achieved by 21.6%, 26.84%, 19.85%, and 14.62%, respectively, in GA, SBO , PSO, and SA based optimizations. The experimental results are compared with a recent publication and proposed GA, SBO, and PSO based methods achieve 18.75%, 4.17%, and 1.04% improvement in terms of average net cut respectively. The proposed GA, SBO, PSO and SA based methods also achieve 44.90%, 51.30%, 40.05%, and 18.60% improvement in terms of average power efficiency.

Three different topologies for 4-bit counters in quantum-dot cellular automata technology

Abstract QCA (Quantum-dot Cellular Automata) technology is an innovative technology based on quantum principles and uses quantum dots as the basic units for information transfer. This technology enables the fabrication of electronic circuits at high speed, lower power consumption and smaller size. For this reason, in this article, for the first time, three types of counters are designed in QCA technology with the least cell and the smallest area. First, Johnson ring counter with 132 cells, area of 0.16 μ m 2 and a delay of 2 clock cycles, then synchronous counter with 192 cells and area of 0.2 μ m 2 and finally synchronous with reset capability with 267 cells and area of ​ 0.27 μ m 2 is designed. Also, the number of cells and the occupied area of the proposed designs have improved by 25.82 % and 35 % , respectively, compared to different authorities. As a result, the proposed designs are among the most optimal designs among different authorities. All the simulations were done in QCAPro and QCADesigner version 2.0.3 softwares.

Charge Transport in Interband Cascade Lasers: An Ab‐Initio Self‐Consistent Model

AbstractInterband cascade lasers (ICLs) stand out due to their low threshold current and minimal power consumption, rendering them viable sources for compact and mobile devices in the mid‐infrared. Since their first demonstration, they experienced major performance improvements. Mostly they originate from either improved material quality or the outcomes of numerical analysis of secluded parts. Encouraged by the impact of secluded models, an ICL‐specific simulation tool can lead to performance breakthroughs and a better comprehension of governing mechanisms. Drawing from an evaluation of existing tools designed for quantum cascade structures, a self‐consistent density matrix rate equation model is implemented to simulate the transport in both conduction and valence band heterostructures. Albeit the extensive inclusion of the quantum effects, special care was taken to maintain a high numerical efficiency. The charge transport model additionally considers optical field calculations, allowing for predictive calculations of light–current–voltage curves. The model is benchmarked against well‐established ICL designs and demonstrate reliable performance predictability. Additionally, detailed insights into device characteristics extracted from the model are provided. This ultimately allows to deepen the understanding of ICL and not only refine existing ones but also generate novel optimized designs.

Recent Developments on Novel 2D Materials for Emerging Neuromorphic Computing Devices

The rapid advancement of artificial intelligent and information technology has led to a critical need for extremely low power consumption and excellent efficiency. The capacity of neuromorphic computing to handle large amounts of data with low power consumption has garnered a lot of interest during the last few decades. For neuromorphic applications, 2D layered semiconductor materials have shown a pivotal role due to their distinctive properties. This comprehensive review provides an extensive study of the recent advancements in 2D materials‐based neuromorphic devices especially in multiterminal synaptic devices, two‐terminal synaptic devices, neuronal devices, and the integration of synaptic and neuronal devices. Herein, a wide range of potential applications of memory, computation, adaptation, and artificial intelligence is incorporated. Finally, the limitations and challenges of neuromorphic devices based on novel 2D materials are discussed. Thus, this review aims to illuminate the design and fabrication of neuromorphic devices based on van der Waals (vdW) heterostructure materials, leveraging promising engineering techniques to excel the applications and potential of neuromorphic computing for hardware implementations.

A Developer-Oriented Framework for Assessing Power Consumption in Mobile Applications: Android Energy Smells Case Study

Currently, people spend a lot of time using their mobile devices. With such ubiquity of mobile devices in our daily life, battery capacity and quality are of utmost importance. Running software applications (called apps) are one of the major factors influencing the power consumption in mobile devices. In order to meet user needs, mobile apps are becoming inherently complex and resource greedy. Therefore, fulfilling these requirements at the pace imposed by the market may degrade software construction quality and insert so-called energy code smells: bad patterns in the source code of an app that indicate a deeper problem and adversely affect power consumption. This work proposes a developer-oriented framework for identifying and fixing patterns via analyzing different application code flavors in a user-driven test scenario. A special app was designed in order to validate the Android implementation of the proposed methodology. The study results have shown significant improvement regarding energy efficiency after correcting one or more energy code smells, with a 4 to 30 percent decrease in battery drain. Additionally, the power consumption signature term is defined in the context of mobile applications. This paper presents a developer-oriented framework for assessing power consumption in mobile applications. Our key contributions include identifying significant energy code smells, demonstrating their impact on power consumption, and providing a toolset for developers to improve energy efficiency.

Design of SRAM cell using an optimized D-latch in quantum-dot cellular automata (QCA) technology

A newer nanoscale technology called quantum-dot cellular automata (QCA) has been used by researchers to design digital circuits in place of the more traditional complementary metal–oxide semiconductor (CMOS) technology. This recent development in the technology change is due to the problems faced by CMOS technology in terms of power consumption and physical limitations. The advantages of QCA technology over CMOS technology are high density, low power consumption, high-speed operation, and less footprint area. This research provides a novel circuit for D-latch and static random access memory (SRAM) cells based on QCA technology. Initially, a D-latch circuit is proposed with a layout area of 0.01 μm2, a 0.5 clock cycle delay (latency), and a cell count of 18 QCA cells. Furthermore, an SRAM cell is proposed using the same D-latch circuit, which uses cell counts of 26 QCA cells and contributes to a layout area of 0.02 μm2 with a 0.75 clock cycle delay (latency). It is observed that our proposed circuits have a smaller layout area, fewer QCA cell counts, and a lower clock cycle delay (latency) than existing circuits.

Schottky barrier memory based on heterojunction bandgap engineering for high-density and low-power retention

Dynamic random-access memory (DRAM) has been scaled down to meet high-density, high-speed, and low-power memory requirements. However, conventional DRAM has limitations in achieving memory reliability, especially sufficient capacitance to distinguish memory states. While there have been attempts to enhance capacitor technology, these solutions increase manufacturing cost and complexity. Additionally, Silicon-based capacitorless memories have been reported, but they still suffer from serious difficulties regarding reliability and power consumption. Here, we propose a novel Schottky barrier memory (SBRAM), which is free of the complex capacitor structure and features a heterojunction based on bandgap engineering. SBRAM can be configured as vertical cross-point arrays, which enables high-density integration with a 4F2 footprint. In particular, the Schottky junction significantly reduces the reverse leakage current, preventing sneak current paths that cause leakage currents and readout errors during array operation. Moreover, the heterojunction physically divides the storage region into two regions, resulting in three distinct resistive states and inducing a gradual current slope to ensure sufficient holding margin. These states are determined by the holding voltage (Vhold) applied to the programmed device. When the Vhold is 1.1 V, the programmed state can be maintained with an exceptionally low current of 35.7 fA without a refresh operation.

Fiber‐Integrated Diamond Quantum Sensor for High‐Voltage Current Measurements

AbstractIn power network systems, there is an urgent demand for highly accurate and miniaturized sensors, owing to their high safety level and limited installation space. Current sensors in high‐voltage grids are required to accommodate harsh environments and provide accurate measurements of several kiloamperes. Thus, this study proposed an integrated quantum diamond sensor to facilitate high‐accuracy, large‐dynamic‐range current measurements. The design incorporated optical fiber and directional microwave (MW) antennas to drive the diamond sensor, which significantly reduced the size and power consumption on the high‐voltage side. Remote‐control and demodulation systems are installed more than 10 m away from the low‐voltage side. The proposed approach achieved zero power consumption on the high‐voltage side and ensured efficient signal transmission. A passive diamond probe manufactured using microfabrication processes facilitated miniaturization and practical deployment. Through parameter optimization, a magnetic detection sensitivity of 4.86 nT·Hz−1/2 is achieved at a safe distance of 11 m, which can be further optimized to 0.77 nT·Hz−1/2 with enhanced MW power. This sensor achieved a current measurement error of ±0.4% in the 1000 A measurement range. Thus, this study provides a new solution for the application of diamond quantum sensors in power systems.

A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs

In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm2, and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.

Development and evaluation of cable-less heating mats utilizing low-power carbon nanotube planar heaters

We aimed to develop a low-power heating mat based on a carbon nanotube (CNT) planar heater and verify its performance by utilizing the heating element. The cell samples were manufactured, and their electrical and thermal properties were evaluated to conduct heating tests. The developed CNT planar heater was tested in four ways, namely heating one cell individually and heating two/three/four cells simultaneously. When operating multiple cells, the resistance decreased, and the power increased. The temperature averaged 37°C even when multiple cells were operating, and the power consumption per unit area was at a similar level. In addition, a prototype heating vest was fabricated using the cells, and a performance comparison was conducted with a third-party heating vest product. The heating efficiency was more than twice as high as that of the third-party product, and the heating area was more than twice as large. Prototype heating pads were made using the cells and compared with two other third-party samples that are heated by thermal wires, and the developed prototype was found to have a heating efficiency more than 20% higher than the other samples, with the lowest temperature deviation. The CNT planar heater developed in this study can be realized in various shapes without wires and can be used safely. It is economical because it can be set to the appropriate temperature in the desired area with a controller, and it is eco-friendly because it prevents mold growth by emitting far-infrared radiation.

Bio‐Voltage Diffusive Memristor from CVD Grown WSe2 as Artificial Nociceptor

AbstractMemristors have emerged as a promising candidate to mimic the human behavior and thus unlocking the potential for bio‐inspired computing advancement. However, these devices operate at a voltages which are still far from the energy‐efficient biological counterpart, which uses an action potential of 50–120 mV to process the information. Here, a diffusive memristor is reported from synthetic WSe2 fabricated in Ag/WSe2/Au vertical device geometry. The devices operate at bio‐voltages of 40–80 mV with Ion/Ioff ratio of 106 and steep switching turn ON and OFF slopes of 0.77 and 0.88 mV per decade, respectively. The power consumption in standby mode and power per set transition are found to be 10 fW and 64 pW, respectively. Further, the diffusive memristors are utilized to emulate the nociceptor, a special receptor for sensory neurons that selectively responds to noxious stimuli. Nociceptor in turn imparts a warning signal to the central nervous system which then triggers the motor response to take precautionary actions to prevent the body from injury. The key features of a nociceptor including “threshold”, “relaxation”, “no‐adaptation” and “sensitization” are demonstrated using artificial nociceptors. These illustrations imply the feasibility of developing low‐power diffusive memristors for bio‐inspired computing, humanoid robots, and electronic skins.

All-optical reconfigurable optical neural network chip based on wavelength division multiplexing

Optical computing has become an important way to achieve low power consumption and high computation speed. Optical neural network (ONN) is one of the key branches of optical computing due to its wide range of applications. However, the integrated ONN schemes proposed in previous works have some disadvantages, such as fixed network structure, complex matrix-vector multiplication (MVM) unit, and few all-optical nonlinear activation function (NAF) methods. Moreover, for the most compact MVM schemes based on wavelength division multiplexing (WDM), it is infeasible to employ intrinsic nonlinear effects to implement NAF, which brings frequent O-E-O conversion in ONN chips. Besides, it is also hard to realize a reconfigurable ONN with coherent MVMs, while it is much easier to implement in WDM schemes. We propose for the first time an all-optical silicon-based ONN chip based on WDM by adopting a new adjustment mechanism: optical gradient force (OGF). The proposed scheme is reconfigurable with tunable layers, variable neurons per layer, and adjustable NAF curves. In the task of classification of the MNIST dataset, our chip can realize an accuracy of 85.13% with 4 full-connected layers and only 50 neurons in total. In addition, we analyze the influence of the OGF-based NAF under fabrication errors and propose a calibration method. Compared to the previous works, our scheme has the two-fold advantages of compactness and reconfiguration, and it paves the way for the all-optical ONN based on WDM and opens the path to unblocking the bottleneck of integrated large-dimension ONNs.

All-In-One Optoelectronic Transistors for Bio-Inspired Visual System.

Visual perception has profound effects on human decision-making and emotional responses. Replicating the functions of the human visual system through device development has been a constant pursuit in recent years. However, to fully simulate the various functions of the human visual system, it is often necessary to integrate multiple devices with different functions, resulting in complex, large-volume device structures and increased power consumption. Here, an optoelectronic transistor with comprehensive visual functions is introduced. By coupling diverse photoreceptive properties of the channel and electrical regulation through charge injection/ferroelectric switching from the hafnium-based gate, the devices can simulate functions of both photoreceptors in the retina and synapses in the visual cortex. A device array is constructed to confirm the perceptual functions of cone and rod cells. Subsequently, color discrimination and recognition for color images are achieved by combining the tunable perception and synapse functions. Then an intelligent traffic judgment system with this all-in-one device is developed, which is capable of making judgments and decisions regarding traffic signals and pedestrian movements. This work provides a potential solution for developing compact and efficient devices for the next-generation bio-inspired visual system.

Real-time Blood Pressure Prediction on Wearable Devices using Edge Based Deep Neural Networks: A Hardware-software Co-design Approach

This paper presents the hardware realization of a real-time blood pressure (BP) prediction model for wearable devices, utilizing long short-term memory (LSTM) deep neural networks (DNNs). The proposed system uses both electrocardiogram (ECG) and photoplethysmogram (PPG) signal values for BP prediction. It aims to address the limitations of traditional BP measurement methods, providing a low error, minimal computational overhead, more accurate and convenient alternative system for individuals with hypertension or at risk for cardiovascular diseases. The utilization of split matrix approach leads to a reduction in hardware complexity across the entire system. This technique involves breaking down the larger weight matrices used in the computations of DNNs into smaller matrices. This fragmentation results in a decrease in the complexity of the hardware responsible for performing matrix vector multiplications (MVMs) within LSTMs. The resultant architecture of the predictive model gains several advantages, including a lowered level of complexity in terms of the space occupied by individual cells, decreased processing delay, and reduced power consumption. Furthermore, this approach enables the achievement of a notably improved minimum achievable clock period of 2.972 ns. This prediction model can operate locally on wearable devices, reducing the reliance on cloud computing and improving privacy and security. The performance evaluations are carried out using both analytical and implementation results. The results indicate that the proposed model can be practically applied to real-world problems and can potentially enhance the accuracy of various machine-learning tasks.