High Power Consumption Research Articles

Neuromorphic Computing in Synthetic Antiferromagnets by Spin‐Orbit Torque Induced Magnetic‐Field‐Free Magnetization Switching

AbstractSynthetic antiferromagnet (SAF) with high thermal stability, ultra‐fast spin dynamics, and highly efficient spin‐orbit torque switching has great application potential in neuromorphic computing hardware. However, two challenges, the weakening of Hall signal in the remanent state and the need for a large auxiliary magnetic field for perpendicular magnetization switching, greatly limit the advantages of SAF in neuromorphic computing. In this work, both the enhanced anomalous Hall resistance and magnetic‐field‐free perpendicular magnetization switching are achieved by using oblique sputtering to fabricate the Pt/CoPt/Ru/CoTb SAF with strong interlayer exchange coupling and magnetic moment compensation. The fabricated SAF as synapse shows nearly linear, nonvolatile multistate plasticity, and as neuron exhibits a nonlinear sigmoid activation function, which are used to construct a fully connected neural network with a remarkable 97.0–98.1% recognition rate for the handwritten digits. Additionally, SAF serving as spike‐timing‐dependent plasticity synapse is used to construct an adaptive, unsupervised learning spiking neural network, and achieve an 87.0% accuracy in handwritten digit recognition. The findings exhibit the promise of SAFs as specialized hardware for high‐performance neuromorphic computing, offering high recognition rates and low power consumption.

Shaping the energy curves of a servomotor-based hexapod robot.

The advantageous versatility of hexapod robots is often accompanied by high power consumption, while animals have evolved an energy efficient locomotion. However, there are a lack of methods able to compare and apply animals' energetic optimizations to robots. In this study, we applied our method to a full servomotor-based hexapod robot to evaluate its energetic performance. Using an existing framework based on the laws of thermodynamics, we estimated four metrics using a dedicated test bench and a simulated robotic leg. We analyzed the characteristics of a single leg to shape the energetic profile of the full robot to a given task. Energy saving is improved by 10% through continuous duty factor adjustment with a 192% increase in power maximization. Moreover, adjusting the robot's velocity by the step length and associating this with gait switching, reduces the power loss by a further 10% at low-speed locomotion. However, unlike in animals, only one unique optimal operating point has been revealed, which is a disadvantage caused by the low energetic efficiency of servomotor-based hexapods. Thus, these legged robots are severely limited in their capacity to optimally adjust their locomotion to various tasks-a counter-intuitive conclusion for a supposedly versatile robot.

Development a policy for the production of Bitcoins with renewable energy sources

Bitcoin, the first decentralized digital currency introduced by an anonymous person or group since 2008, has attracted worldwide attention. A significant number of economists have introduced Bitcoin as a new phenomenon in the 21st century that could reduce global inflation. Given the tens of thousands of digital currencies that have emerged since the advent of Bitcoin and its price growth trend over more than a decade, which are signs of the growth of this business. In addition to being money, Bitcoin has always been considered a tool for investing and storing value, which is why it is called digital gold. One of the most important problems in the production or extraction of Bitcoins is the high-power consumption by miners. If the energy sources of electricity generation are supplied by non-renewable energy sources, in addition to emitting air pollutant gases, it will increase greenhouse gases and consequently contribute to climate change. In this research, based on the idea of the authors, which is that the economic support of Bitcoin is energy, a strategy for producing Bitcoin from renewable energy sources is considered. First, the amount of electrical energy consumption by Bitcoin production is calculated based on statistical data, and then based on the price of electricity in different countries of the world and its global average, the base price of Bitcoin is calculated. In the following, four scenarios are proposed for the production of Bitcoin by electricity supplied from non-renewable energy sources. These scenarios include coal-fired steam power plants, natural gas-fired power plants, natural gas/oil gas-fired power plants, and dual-cycle (steam and gas cycles) natural gas-fired power plants. Based on the amount of electricity required to produce one Bitcoin, the amount of pollutants emitted to produce Bitcoin and its social costs are calculated. These costs should be added to the base cost of Bitcoin production if non-renewable energy sources are used to produce Bitcoin. Then, renewable energy sources for Bitcoin production based on the price of electricity generated by renewable energy sources are examined. Based on the analyses, how to choose the best renewable energy source to produce Bitcoin is presented as a scenario. This article briefly answers two key questions: 1. At what price of Bitcoin is it cost-effective for governments to produce it? 2. What is the best renewable energy source to produce it? These two questions can be useful in creating a roadmap and strategy for economists and governments.

The Goldilocks principle of learning unitaries by interlacing fixed operators with programmable phase shifters on a photonic chip

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Programmable photonics provides a flexible platform that can be reconfigured to perform multiple tasks, thereby holding great promise for revolutionizing future optical networks and quantum computing systems. Over the past decade, there has been constant progress in developing several different architectures for realizing programmable photonic circuits that allow for realizing arbitrary discrete unitary operations with light. Here, we systematically investigate a general family of photonic circuits for realizing arbitrary unitaries based on a simple architecture that interlaces a fixed intervening layer with programmable phase shifter layers. We introduce a criterion for the intervening operator that guarantees the universality of this architecture for representing arbitrary N×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N \ imes N$$\\end{document} unitary operators with N+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N+1$$\\end{document} phase layers. We explore this criterion for different photonic components, including photonic waveguide lattices and meshes of directional couplers, which allows the identification of several families of photonic components that can serve as the intervening layers in the interlacing architecture. Our findings pave the way for efficiently designing and realizing novel families of programmable photonic integrated circuits for multipurpose analog information processing.

Onboard cooperative relative positioning system for Micro-UAV swarm based on UWB/Vision/INS fusion through distributed graph optimization

In recent years, the swarm of micro Unmanned Aerial Vehicles (micro-UAVs) is becoming a research hotspot. As the basis of swarm autonomous flight, traditional relative positioning methods typically rely on external base stations or onboard sensors of high power consumption, the vast majority of which are not feasible on resource-constrained micro-UAVs flying in a complex environment. This article proposes a lightweight relative positioning system implemented on a micro-UAV platform weighing 150 g. We have developed a distributed graph optimization (DGO) scheme fusing the results of the onboard Ultra-Wideband (UWB) module, camera, and inertial sensors. A Delay Extended Kalman Filter (DEKF) is utilized to mitigate communication delays and enhance accuracy. This scheme enables micro-UAVs to collaboratively compute the relative position graph in real-time. Through real-world experiments, our system has achieved a relative positioning accuracy of 0.167 m, exhibiting significant potential for future swarm applications.

Design of synthesizable period-jitter sensor IP with high power reduction and variation resiliency

Previous work has presented a synthesizable design approach to ease the design of an on-chip period-jitter sensor (PJS) with a high resolution. Although the designer of a very large scale integration (VLSI) chip hopes to use this design as an intellectual property (IP), our analysis reveals that this PJS faces key challenges: high power consumption and vulnerability to static PVT and dynamic IR-drop variations. This work develops several design techniques to conquer these challenges at the same time. Taking the PJS IP for monitoring the clock signal in LPDDR4-4266 as a design example, we implement a synthesized 22 nm 2.133 GHz PJS with a resolution of 1.0 ps to verify the design techniques. Post-layout simulation results show that the new design reduces over half of the power while meeting the resolution specification. It passes functional and electrical verification over a broader process variation than the previous design, and the higher variation resiliency assists the synthesizable Verilog code as a soft IP.

OpSAVE: Eviction Based Scheme for Efficient Optical Network-on-Chip

For on-chip networks, nanophotonics has been considered a strong alternative owing to its high speed (due to low latency) and high bandwidth (due to wavelength division multiplexing). However, the major hurdle in the adoption of nanophotonic-based on-chip networks is their high static power consumption. Various proposals are there in the literature which try to reduce the static power consumption either by modulating the laser or by allowing the on-chip stations to share the photonic channels. In this paper, we propose OpSAVE— an optical NoC that combines the above two strategies to effectively reduce static power consumption. It proposes a superior prediction mechanism based on the eviction details from the private caches. It explains how shared channels can be used to dynamically balance the load and at the same time handle mispredictions. It allows the optical stations to share both the power and the available bandwidth to increase their utilization. Moreover, OpSAVE proposes to use a double pumping strategy to improve the system performance. We compared our scheme with the state-of-the-art proposals in this domain and the results show that our scheme consumes 4.4X less optical power and at the same time improves the performance by nearly 28%. In the evaluation, we have considered the multicore benchmarks from the Splash and Parsec benchmark suites.

Synergistic Effect of ZIF-8 and Pt-Functionalized NiO/In2O3 Hollow Nanofibers for Highly Sensitive Detection of Formaldehyde.

A rapid and accurate monitoring of hazardous formaldehyde (HCHO) gas is extremely essential for health protection. However, the high-power consumption and humidity interference still hinder the application of HCHO gas sensors. Hence, zeolitic imidazolate framework-8 (ZIF-8)-loaded Pt-NiO/In2O3 hollow nanofibers (ZPNiIn HNFs) were designed via the electrospinning technique followed by hydrothermal treatment, aiming to enable a synergistic advantage of the surface modification and the construction of a p-n heterostructure to improve the sensing performance of the HCHO gas sensor. The ZPNiIn HNF sensor has a response value of 52.8 to 100 ppm HCHO, a nearly 4-fold enhancement over a pristine In2O3 sensor, at a moderately low temperature of 180 °C, along with rapid response/recovery speed (8/17 s) and excellent humidity tolerance. These enhanced sensing properties can be attributed to the Pt catalysts boosting the catalytic activity, the p-n heterojunctions facilitating the chemical reaction, and the appropriate ZIF-8 loading providing a hydrophobic surface. Our research presents an effective sensing material design strategy for inspiring the development of cost-effective sensors for the accurate detection of indoor HCHO hazardous gas.

Piezo‐Turned Magnet Rotation for ELF/SLF Cross‐Medium Communication in Omni‐Direction

AbstractExtremely/super low frequency (ELF/SLF) electromagnetic (EM) waves offer efficient propagation in challenging cross‐medium environments. However, conventional coil antennas, when emitting ELF/SLF EM waves, face constraints related to size, radiation efficiency, and high power consumption. To overcome these limitations, a disc‐rod structured piezoelectric rotating resonator is proposed, which can turn a NdFeB permanent magnet disc into rotation with an impressive high‐speed up to 10 000 r min−1 via wobbling motion of the rod tip, effectively generating ELF/SLF magnetic field radiation via rotational oscillation other than common linear and swinging oscillation of the magnetic dipole moments. The piezo‐turned magnetic rotation (PTMR) method leads to a two to five‐fold enhancement in magnetic field emission compared to existing piezo‐actuated acoustic resonant antennas, and a 370 times higher emitting efficiency compared to the traditional coil antenna with a comparable size. In contrast to unidirectional EM wave radiation at their solely resonant frequencies, the PTMR antenna showcases omnidirectional B‐field radiation with continuous tunability in a dual frequency band spanning from 10 to 100 Hz. Moreover, an air–seawater cross‐medium magnetic field communication is successfully demonstrated. With outstanding performance metrics, the PTMR antenna emerges as a promising solution for efficient long‐distance ELF/SLF cross‐medium communication.

Low self-starting threshold polarization-maintaining Er-doped fiber optical frequency comb

We report the development of an all-fiber polarization maintaining (PM) optical frequency comb using a mode-locked figure-9 laser with a low self-starting pump threshold. We have achieved self-starting mode-lock for repetition rates (fr) from 70 MHz to 109 MHz. At a repetition rate of 109 MHz, mode-locking can be achieved for a pump power ranging from 187 mW to 880 mW. To the best of our knowledge, this is the lowest pump power reported for PM figure-9 erbium fiber lasers with repetition rate over 100 MHz. By optimizing the pump power to 238 mW, we have achieved an output power of 5 mW, center wavelength of 1566.2 nm, and 3-dB spectral bandwidth of 20.5 nm. The repetition rate has a high signal-to-noise ratio of 95 dB at a resolution bandwidth of 300 Hz. We have studied the spectral characteristics of the laser under different cavity lengths and pump powers. Additionally, we have stabilized the repetition rate using a GPS-Rb disciplined RF reference. The fractional instability of the repetition rate is measured to be 4.67×10-12 at 1 s and 9.22×10-13 at 10 s over a measurement of 11 h. Our findings demonstrate that the developed figure-9 comb is robust, compact and has the advantage of high stability and low power consumption. It offers a cost effective solution for future outdoor comb applications.

Bit-tunable XOR operation based on photonic spiking neuron

The XOR logic operation is an important element for photonic computing, but its implementation is limited by high power consumption, complex determination rule of the output signal and polarization sensitive. By utilizing the spiking excitation characteristics of photonic spiking neuron, we propose a polarization-insensitive XOR operation architecture which has the advantage of the tunable bit interval by changing the delay of feedback loop. We simulatively demonstrate that XOR operation with arbitrary bit interval can be realized with zero-error rate by tuning the variable optical delay line. Besides, when introducing a noise with an SNR = 10 dB or a time jitter of 2μs (10% of the unit length of input spike) to the input spike, the error rate of XOR can still reach zero, showing a strong robustness of our system. Experiment for adjacent-bit XOR operation is further implemented and zero error-rate is achieved, verifying the feasibility of our proposed system.

A study on robust design of cruciform torsional beam/cantilever beam

The electro-thermal actuator of a micro-electromechanical system (MEMS) has high power consumption and low response speed. To overcome this shortcoming, a steady state mechanism of cross torsion beam/cantilever beam is proposed in conjunction with the micro-electro-thermal actuator. Through the analysis of the structure and working principle of the cross torsion beam/cantilever beam, as well as the randomness analysis of its design variables and noise factors, the robust design is carried out, and the algorithm program of the stochastic model is written to solve the robust design model. The results show that the robust design method can not only ensure that the cantilever beam will not collapse excessively after the whole mechanism is released from the air but also ensure the robustness of the design solution when the design variables and noise factors change.

Design of GRETA Cooling Systems

The Gamma-Ray Energy Tracking Array (GRETA) is a full 4π gamma-ray tracking detector capable of reconstructing the energy and three-dimensional position of gamma-ray interactions within a compact sphere of high-purity germanium crystals. The GRETA Detector Array Sphere will have the capacity to accommodate a total of 30 Germanium Quad Detector Modules (QDM). The 30 QDMs are to be cooled and maintained below 100 K using liquid nitrogen (LN) at all times while the array is in normal operation, and will require regular filling of a LN Dewar on each module. The Dewar is designed to allow the Quad Module to be operated in any orientation with a LN holding time of no less than 12 hours when the detector module is fully powered. An automated LN cooling and refilling system is required to supply LN to the 30 QDMs and ensure them maintained below 100 K. Each of the GRETA QDMs houses a total of 148 pre-amplifier units within the module, and with the high power consumption of each pre-amplifier, active cooling of the pre-amplifier compartment is required. Additionally, each Quad Module will have 4 digitizer modules attached to it, which generate heat and require cooling as well. A closed-loop liquid (Glycol) cooling system will provide the required temperature stability and dissipate power generated heat for electronics. This paper presents design of the GRETA LN cooling system for detectors and the closed-loop liquid cooling system for electronics including technical requirements, design schemes, key components, operation modes, and so on.

Biological plausible algorithm for seizure detection: Toward AI-enabled electroceuticals at the edge

Nearly 1% of people worldwide suffer from epilepsy. Electroencephalogram (EEG)-based diagnostics and monitoring tools, such as scalp EEG, subscalp EEG, stereo EEG, or sub/epi-dural EEG recordings [also known as electrocorticography (ECoG)], are widely used in different settings as the gold standard techniques to perform seizure identification, localization, and more primarily in epilepsy or suspected epilepsy in patients. Techniques such as subscalp EEG and ECoG offer long-term brain interaction, potentially replacing traditional electroceuticals with smart closed-loop therapies. However, these systems require continuous on-device training due to real-time demands and high power consumption. Inspired by the brain architecture, biologically plausible algorithms, such as some neuromorphic computing, show promise in addressing these challenges. In our research, we utilized liquid time-constant spiking neural networks with forward propagation through time to detect seizures in scalp-EEG. We trained and validated our model on the Temple University Hospital dataset and tested its generalization on out-of-sample data from the Royal Prince Alfred Hospital (RPAH) and EPILEPSIAE datasets. Our model achieved high area under the receiver operating characteristic curve (AUROC) scores of 0.83 in both datasets. We assessed the robustness by decreasing the memory size by 90% and obtained an overall AUROC of 0.82 in the RPAH dataset and 0.83 in the EPILEPSIAE dataset. Our model showed outstanding results of 3.1 μJ power consumption per inference and a 20% firing rate during training. This allows for incorporating bio-inspired efficient algorithms for on-device training, tackling challenges such as memory, power consumption, and efficiency.

Bioinspired Potentiometric and Passive Strain Sensing Based on Mechanical Regulation of Ionically Conductive Percolating Networks

AbstractFlexible strain sensors are essential components for wearable devices, human‐machine interaction systems, and intelligent soft robots. Current active strain sensors have high power consumption, while passive strain sensors exhibit limitations in detecting static or slowly‐varying stimulations. Here, inspired by the strain sensing behavior of Ruffini endings in natural skin, a new type of potentiometric, fully passive, and highly stretchable strain sensors are presented based on the mechanical regulation of ionically conductive percolating networks. Specifically, an artificial potential difference is first created with two electrochemical active electrodes, followed by encoding external strain stimulations into the potential difference variations using a new class of highly elastic and ionically conductive composites with configurable ionic percolating networks. Based on the proposed passive strain sensing mechanism, both static and slowly‐varying strain stimulations (up to 100%) can be monitored with ultralow power consumption (at nW level). Promising applications of the passive strain sensors in human motion monitoring, Morse code communication, and hand gesture recognition are demonstrated. This work opens up a new avenue to constructing novel strain sensing devices with high stretchability, ultralow power consumption, and desirable sensing performance.

Design and implementation of intelligent anti-hail net system based on STM32

This research is dedicated to designing an intelligent anti-hail net system using STM32 microcontroller. The system integrates sensing technology and communication modules to monitor various environmental parameters in real time, analyzes the data through the STM32 processor, and realizes real-time intelligent control of the anti-hail network. The system design focuses on the collaboration of hardware and software, taking full advantage of the high performance and low power consumption of the STM32 microcontroller. Through actual tests, the feasibility of the system in improving hail prevention effect, real-time performance and automation was verified, providing reliable guarantee for agricultural production. This research provides a useful reference for the application and promotion of smart agricultural technology.

Emergence of Ferroelectricity in p‐Type 2D In1.75Sb0.25Se3

p‐type 2D ferroelectric semiconductors (2D FeSs) play an increasingly essential role in the advanced nonvolatile and morphotropic beyond‐Moore electronic devices with high performance and low power consumption. But reliable p‐type 2D FeS with holes as majority carriers are still scarce. Herein, the first experimental realization of room‐temperature ferroelectricity in van der Waals layered β‐In1.75Sb0.25Se3 down to few layer is reported. The origin of ferroelectricity in β‐In1.75Sb0.25Se3 comes from aliovalent elemental substitution, antimony substituting to the indium sites (SbIn), changing the local environment of the central‐layer Se atoms. Thanks to the intrinsic ferroelectric and semiconducting natures, FeS field‐effect transistor (FeSFET) devices based on β‐In1.75Sb0.25Se3 exhibit reconfigurable, multilevel nonvolatile memory (NVM) states, which can be successively modulated by gate voltage stimuli. Furthermore, the inherent operation mechanism, due to the switchable polarization, indicates that a neuromorphic memory is also possible with 2D FeSFETs. These presented results facilitate the technological implementation of versatile 2D FeS devices for next‐generation logic‐in‐memory approach for Internet‐of‐Things entities.

High-performance efficient embedded systems for impedance spectroscopy: Challenges and potentials

Impedance spectroscopy is nowadays in use in a wide range of applications where there is a particular need for efficient embedded measurement systems. Several embedded impedance spectrometers based on Field Programmable Gate Array (FPGA) have been proposed but have several drawbacks, notably high power consumption, high cost, and complex development frameworks. Meanwhile, microcontrollers have become more powerful, enabling the efficient design of embedded impedance spectrometers. A major challenge, however, is to achieve high measurement performance in terms of accuracy while complying with the conditions for impedance spectroscopy. This paper addresses the potential of microcontroller-based system design for embedded impedance spectrometers and proposes a methodology to comprehensively design compact and efficient microcontroller-based impedance measurement systems. After a review of possible measurement methods, we focus on the optimization of multi-sine excitation signals,the acquisition of current and voltage signals, and the efficient signal analysis, which are key elements in realizing embedded impedance measurement systems that achieve high quality measurements while maintaining linearity and system stability conditions. We demonstrate the efficiency of the proposed design methodology in the two application scenarios of battery diagnosis and bioimpedance spectroscopy.

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

LoRa technology contributes to green energy by enabling efficient, long-range communication for the Internet of Things (IoT). This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa, where the network performance is affected by the density of gateways (GWs) and end devices (EDs), as well as environmental conditions. To mitigate interference, data throughput losses, and high-power consumption, the proposed spreading factor (SF) and hybrid (data rate|SF) models dynamically adjust the transmission parameters. The orchestration of concurrent data modifications within the network server (NS) is crucial for uninterrupted communication between GWs and EDs, especially in monitoring electric vehicle (EV) stations to reduce traffic congestion and pollution. Employing K-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms optimizes ED allocation, averts data congestion, and improves the signal-to-interference noise ratio (SINR). These methods ensure seamless information reception by meticulously allocated EDs across various GW combinations. To estimate the free-space losses (FSL), a log-distance path loss model (log-PL) is used. Exploring various bandwidths (BWs), bidirectional communications, and duty cycles (DCs) helps to prevent saturation, thus prolonging the operational lifespan of EDs. Empirical findings reveal a notable packet rejection rate (PRR) of 0% for the DBSCAN (hybrid model). In contrast, the K-means exhibits a PRR ranging from 5% (hybrid model) to 35.29% (SF model) for the ten GWs combination. Notably, the network saturation is reduced to 10.185% and 9.503%, respectively, highlighting an improvement in the average efficiency of slotted ALOHA (91.1%) and pure ALOHA (90.7%). These enhancements increase the lifespan of EDs to 15,465.27 days.

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.