Asynchronous Operation Research Articles

Multi-Area Asynchronous Grid Operation With Frequency Reserve Sharing

Multi-Area Asynchronous Grid Operation With Frequency Reserve Sharing

Integer Factorization with Stochastic Spiking in Percolating Networks of Nanoparticles.

As growth in global demand for computing power continues to outpace ongoing improvements in transistor-based hardware, novel computing solutions are required. One promising approach employs stochastic nanoscale devices to accelerate probabilistic computing algorithms. Percolating Networks of Nanoparticles (PNNs) exhibit stochastic spiking, which is of particular interest as it meets criteria for criticality which is associated with a range of computational advantages. Here, we show several ways in which spiking PNNs can be used as the core stochastic components of coupled networks that allow successful factorization of integers up to 945. We demonstrate asynchronous operation and show that a single device is sufficient to solve all factorization tasks and to generate multiple solutions simultaneously.

Adaptive protocol-based control for reaction-diffusion memristive neural networks with semi-Markov switching parameters

This study explores the asynchronous control of reaction-diffusion memristive neural networks (RDMNNs) using an innovative adaptive event-triggered protocol. The unique characteristic of RDMNNs is captured through a semi-Markov process model, wherein the probability density function of the duration time is contingent on two consecutive modes. A novel adaptive event-triggered strategy, specifically designed for the semi-Markov switching signal, is introduced to effectively reduce the network's bandwidth usage. The determination of thresholds in the adaptive triggering criterion is intricately associated with the system state residuals. Due to the mismatch between the controller and the RDMNNs, the protocol-based controller operates asynchronously. This asynchronous operation is characterized by a hidden semi-Markovian model. Utilizing stochastic Lyapunov functions that correlate with the detected and system modes, several sufficient criteria for designing an effective asynchronous controller are provided, thereby ensuring the stochastic stability of the system. Finally, the feasibility of the proposed scheme is validated through a simulated example.

МОДЕЛЮВАННЯ ВЗАЄМОДІЇ В РОЗПОДІЛЕНИХ ІНТЕЛЕКТУАЛЬНИХ ІНФОРМАЦІЙНИХ СИСТЕМАХ МЕТОДАМИ АВТОМАТИЧНОГО ПЛАНУВАННЯ

This paper delves into the intricacies of resource management and task scheduling within distributed information systems, aiming to propose a processing flow for effective coordination and information exchange among system components. The research focuses on the coordination of actions and information exchange within distributed intelligent systems, highlighting the issues stemming from asynchronous operations, resource management complexities, and the necessity to adapt to dynamic environmental conditions. By leveraging these challenges, the study presents results showcasing the impact on task execution delays, suboptimal resource utilization, and overall system performance degradation. Through experimentation, it underscores the complexity inherent in coordinating distributed intelligent systems and emphasizes the need for an efficient synchronization mechanism. The proposed approach aims to mitigate these challenges, offering insights into modeling, automatic planning, and optimization within information systems. Context. The paper focuses on addressing the challenges associated with resource management and task scheduling in distributed information systems. Objective. The purpose of the work is to propose the processing flow for coordination and information exchange between different components of distributed intelligent systems. Methods. The paper presents the research results about coordination of actions and information exchange between components of distributed intelligent systems. These problems can lead to delays in the execution of tasks, inefficient use of resources, and a general decrease in system performance. These difficulties are caused by a variety of factors, including the asynchronous operation of components, the complexity of resource management, and the need to adapt to changing environmental conditions. In current research, we utilized the asynchronous operation of components, the complexity of resource management, and the need to adapt to changing environmental conditions. Results. The paper presents the research results about coordination of actions and information exchange between components of distributed intelligent systems. Conclusions. The experiments confirmed the complexity of coordination and information exchange between these components, which can lead to delays in the execution of tasks, under-optimized use of resources and a general deterioration in system performance. This research was aimed at developing an effective way to sync and coordinate distributed intelligent information systems, including the asynchronous operation of components, the complexity of resource management, and the need to adapt to changing environmental conditions

MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks

<p>In recent years, with the rapid development of science and technology, many new technologies have made people’s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol based on the Slotted-FAMA of Multiple Reception (MR-SFAMA) protocol. The protocol uses the Q-Learning algorithm to optimize the multi-receiver handshake mechanism. The current state is judged according to the received node request, and the Q-table is established. Through the multi-round interaction between the node and the environment, the Q-table is continuously updated to obtain the optimal strategy and determine the optimal data transmission scheduling scheme. The reward function is set according to the total back-off time and frame error rate, which can reduce the packet loss rate during network data transmission while reducing the delay. In addition, the matching asynchronous operation and uniform random back-off algorithm are used to solve the problem of long channel idle time and low channel utilization. This new protocol can be well applied to unstable network topology. The simulation results show that the protocol performs better than Slotted-FAMA and MR-SFAMA regarding delay and normalized throughput.</p> <p> </p>

Fine-grain Quantitative Analysis of Demand Paging in Unified Virtual Memory

The abstraction of a shared memory space over separate CPU and GPU memory domains has eased the burden of portability for many HPC codebases. However, users pay for ease of use provided by system-managed memory with a moderate-to-high performance overhead. NVIDIA Unified Virtual Memory (UVM) is currently the primary real-world implementation of such abstraction and offers a functionally equivalent testbed for in-depth performance study for both UVM and future Linux Heterogeneous Memory Management (HMM) compatible systems. The continued advocacy for UVM and HMM motivates improvement of the underlying system. We focus on UVM-based systems and investigate the root causes of UVM overhead, a non-trivial task due to complex interactions of multiple hardware and software constituents and the desired cost granularity. In our prior work, we delved deeply into UVM system architecture and showed internal behaviors of page fault servicing in batches. We provided quantitative evaluation of batch handling for various applications under different scenarios, including prefetching and oversubscription. We revealed that the driver workload depends on the interactions among application access patterns, GPU hardware constraints, and host OS components. Host OS components have significant overhead present across implementations, warranting close attention. This extension furthers our prior study in three aspects: fine-grain cost analysis and breakdown, extension to multiple GPUs, and investigation of platforms with different GPU-GPU interconnects. We take a top-down approach to quantitative batch analysis and uncover how constituent component costs accumulate and overlap, governed by synchronous and asynchronous operations. Our multi-GPU analysis shows reduced cost of GPU-GPU batch workloads compared to CPU-GPU workloads. We further demonstrate that while specialized interconnects, NVLink, can improve batch cost, their benefits are limited by host OS software overhead and GPU oversubscription. This study serves as a proxy for future shared memory systems, such as those that interface with HMM, and the development of interconnects.

Changing torque-force synchronization condition for abrasive particle improves material removal during silicon carbide abrasive machining

Silicon carbide (SiC) is extremely hard and brittle, posing challenges for traditional abrasive machining. This study used molecular dynamics simulations to investigate synchronous versus asynchronous force-torque effects on SiC abrasive machining. Multi-grit diamond abrasion of 4H-SiC was modeled by applying controlled sinusoidal forces and torques. Asynchronous force-torque increased material removal rates up to 2X versus synchronous conditions by promoting particle agglomeration and enabling trapped particles to contribute. However, asynchronous operation also produced higher surface roughness and subsurface cracking from greater penetration. The alternating forces generated larger stresses that may have driven more plastic deformation and brittle fracture. The study provided atomic-scale insights into force-torque effects, establishing guidelines to potentially enhance efficiency through asynchronous operation despite drawbacks.

Method for More Accurate Location of the Electrical Center of the Swing in Asynchronous Operation

Method for More Accurate Location of the Electrical Center of the Swing in Asynchronous Operation

On Composing Asynchronous Operations

Asynchronous operations are very useful for actions that wait for an external event or work for a long time, to avoid blocking the thread that launches them. Unfortunately, whether they report their termination via callbacks or via completing a future, composing several asynchronous calls is difficult and error prone. The continuations mechanism (provided, for example, in C# Task Parallel Library via ContinueWith()) offers limited support for scheduling a sequence of operations. In this paper we try to improve this mechanism with better support for sequencing operations and exceptions, and with support for conditionals and loops, while covering the specifics of a C++ implementation. The most recent version of our source code is at [14]. Keywords and phrases: asynchronous programming.

Managing asynchronous workloads in a multi‐tenant microservice enterprise environment

AbstractA multi‐tenant microservice architecture involving components with asynchronous interactions and batch jobs requires efficient strategies for managing asynchronous workloads. This article addresses this issue in the context of a leading company developing tax software solutions for many national and multi‐national corporations in Brazil. A critical process provided by the company's cloud‐based solutions encompasses tax integration, which includes coordinating complex tax calculation tasks and needs to be supported by asynchronous operations using a message broker to guarantee order correctness. We explored and implemented two approaches for managing asynchronous workloads related to tax integration within a multi‐tenant microservice architecture in the company's context: (i) a polling‐based approach that employs a queue as a distributed lock (DL) and (ii) a push‐based approach named single active consumer (SAC) that relies on the message broker's logic to deliver messages. These approaches aim to achieve efficient resource allocation when dealing with a growing number of container replicas and tenants. In this article, we evaluate the correctness and performance of the DL and SAC approaches to shed light on how asynchronous workloads impact the management of multi‐tenant microservice architectures from delivery and deployment perspectives.

An Analog Side-Channel Attack on a High-Speed Asynchronous SAR ADC Using Dual Neural Network Technique

This brief presents a side-channel attack (SCA) technique on a high-speed asynchronous successive approximation register (SAR) analog-to-digital converter (ADC). The proposed dual neural network based on multiple noise waveforms separately discloses sign and absolute value information of input signals which are hidden by the differential structure and high-speed asynchronous operation. The target SAR ADC and on-chip noise monitors are designed on a single prototype chip for SCA demonstration. Fabricated in 40 nm, the experimental results show the proposed attack on the asynchronous SAR ADC successfully restores the input data with a competitive accuracy within 300mV rms error.

AI Proctor: AI-Based Platform for Remote Learning

Progressions in technology give headway to remote exams as a good alternative to on-site proctoring. Due to the COVID-19 pandemic, educators and institutions have been forced to rely on remote synchronous and asynchronous operations. The rapid change left weaknesses in the old systems to surface. Furthermore, it is questionable that they are equal in function to on-site proctoring. Grounded on thorough research, I determined the operation requirements for AI Proctor, a solution that solves many of the concerns with remote proctoring.

Design and Implementation of Ambiently Powered Internet of Things-That-Think With Asynchronous Inference

This work offers design and implementation of in-network inference, using message passing among ambiently powered wireless sensor network (WSN) terminals. The stochastic nature of ambient energy harvesting dictates intermittent operation of each WSN terminal and as such, the message passing inference algorithms should be robust to asynchronous operation. It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, in-network message passing problem, often at the expense of increased total delay. Examples from Gaussian belief propagation and Average Consensus are provided, along with the derivation of a statistical convergence metric for the latter case. A k-means method is offered that maps the elements of the calculated vector to the different WSN terminals and overall execution delay (in number of iterations) is quantified. Interestingly, it is shown that there are divergent instances of the in-network message passing algorithms that become convergent, under asynchronous operation. Ambient solar energy harvesting availability is also studied, controlling the probability of successful (or not) message passing. Hopefully, this work will spark further interest for asynchronous message passing algorithms and technologies that enable in-network inference, towards ambiently-powered, batteryless Internet-of-Things-That-Think.

Design and implementation of low power Advanced Encryption Standard cryptocore utilizing dynamic pipelined asynchronous model

The Advanced Encryption Standard (AES) has added new dimension to cryptography with its potentials of safeguarding the health care devices and systems. This paper presents the design of low power VLSI architecture for AES crypto application focusing on ECG signal transmission. The proposed design addresses the next generation cyber physical security requirement and low power for portable biomedical digital assistant devices. The proposed work utilizes on-chip built-in BRAM, configurable pipelining stages for combinational path, clock gating mechanism, and dynamic pipelined asynchronous operation. The use of dynamic pipelined asynchronous model (DPAM) controls different functional units of AES in controlled sequential order with gating of global clock and reset signals. This prevents unwanted switching activity in the functional units that waits for valid data. The performance of the proposed low power DPAM-AES architecture is compared with existing low power architectures in terms of power consumption, area, delay and throughput. The proposed DPAM-AES architecture results in power consumption starting from 260 mW to 329 mW, which are 47 mW to 385 mW lesser power for the frequencies of 100 MHZ to 800 MHZ respectively. The low power AES micro architecture is designed using VHDL and simulated, synthesized and implemented using Vivado design suite targeting ZynqSoC 7z020clg484-1 FPGA platform. The performance of the proposed AES micro architecture can be further improved by targeting to ASIC implementation of the designed AES core.

Design of Antenna Polarization Plane for Concurrent Uplink/Downlink Drone Networks

In recent years, drones have been used in a wide range of fields, such as agriculture, transportation of goods, and security. Drones equipped with communication facilities are expected to play an active role as base stations in areas where ground base stations are unavailable, such as disaster areas. In addition, asynchronous operation is being considered for local 5G, in order to support all kinds of use cases. In asynchronous operation, cross-link interference between base stations is an issue. This paper attempts to reduce the interference caused by the drone network by introducing circularly polarized antennas against the conventional system using linearly polarized antennas. Numerical analyses are conducted to validate the effectiveness of the proposed system, where Signal-to-Interference Ratios (SIRs) are shown to be improved significantly as the numerical evaluation results. Specifically, for the scenario of only access links, in the region where conventional antenna architecture can only achieve an SIR of less than 20 dB, our proposed system applying circularly polarized antennas can almost realize an SIR of more than 40 dB. Significant improvement can be also observed in the scenario with the existence of backhaul links, where the conventional system had difficulty achieving our system design goal SIR of 16.8 dB, while the proposed antenna architecture could easily attain this goal in most regions of our evaluation ranges.

A multi-arm robot system for efficient apple harvesting: Perception, task plan and control

Robot harvesting has emerged as an urgent need for apple industry due to a sharp decline in agricultural labor. In the field of harvesting robots, the use of multiple robotic arms to improve operational efficiency and promote industrial applications has gained significant attention. Despite the significant progress made in multi-arm harvesting robots in recent years, their widespread application in orchard production is hindered by insufficient efficiency of operation and the accuracy of fruit positioning. This paper focuses on the precise perception and multi-arm collaborative control issues of harvesting robots, and proposes a multi-arm apple harvesting robot system. Firstly, the paper introduces the hardware and software integration method and kinematic configuration of the robot, and presents its workspace division and asynchronous sequential operation mode. Secondly, the paper proposes a stereo vision fruit recognition and localization algorithm based on multi-task deep learning to enhance the accuracy of apple fruit positioning and a method of combining multiple perspectives to acquire a global fruit map is introduced. Finally, the paper presents a multi-arm task planning method based on the Markov game framework to optimize the target harvesting order of each arm and improve the collaboration efficiency. The effectiveness of the robot and its perception and control methods are verified through multiple field experiments in orchards. The field trials showed that the proposed vision system reduces the median locating error of the robot system by up to 44.43%; the proposed task planning algorithm can reduce the average cycle time by 33.3% compared to the heuristic-based algorithm, and time taken for optimizing task planning ranged from 1.14 s to 1.21 s; and the robot’s harvest success rate varied from 71.28% to 80.45%, and the average cycle time ranged from 5.8 s to 6.7 s.

Evaluation of the efficiency of implementation of asynchronous computing algorithms using coroutines and threads in С++

Modern multi-core systems are most effective when used in large server centers and for cloud computing. However, despite the known complexity of software implemen-tation, parallel computing on multiprocessors is increasingly used in computer model-ling. Advanced mechanisms of synchronous and multithreaded programming are in-creasingly used to improve the productivity of numerical studies, reducing the time of computer models implementation. One such mechanism is coroutines, a convenient tool for managing asynchronous operations introduced in the C++20 standard. A special feature of coroutines is the ability to suspend a function at a certain stage, saving its state, and after some time resume its execution from the previous stop. The aim of this research is to improve the performance of computer modelling by using coroutines and data threads. As a result of the work, a test algorithm for multiplying a matrix by a vector and its modified asynchronous version using the coroutine mechanism and splitting into two data threads was developed, which allowed to achieve 1.94 times increase in the com-puting speed when the matrix dimension is 15000 (2.25×106 elements). It has been found that at a small matrix dimension, the developed asynchronous algorithm using coroutines and splitting into two threads is less efficient than the single thread algo-rithm. This is due to the fact that the compiler needs some time to create threads and start execution simultaneously. With a large dimensionality, the performance of the asynchronous algorithm increases significantly. With a matrix dimension of more than 1200, the use of an asynchronous algorithm divided into two threads is guaranteed to be more efficient than a single-threaded. The data obtained are consistent with the results of similar studies of the problem of increasing the efficiency of computer modelling using alternative software and hard-ware. The new method of solving the problems of asynchronous programming provides a more efficient and simple mechanism for managing asynchronous operations.

DR-ALOHA-Q: A Q-Learning-Based Adaptive MAC Protocol for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are challenged by the dynamic nature of the underwater environment, large propagation delays, and global positioning system (GPS) signal unavailability, which make traditional medium access control (MAC) protocols less effective. These factors limit the channel utilization and performance of UASNs, making it difficult to achieve high data rates and handle congestion. To address these challenges, we propose a reinforcement learning (RL) MAC protocol that supports asynchronous network operation and leverages large propagation delays to improve the network throughput.he protocol is based on framed ALOHA and enables nodes to learn an optimal transmission strategy in a fully distributed manner without requiring detailed information about the external environment. The transmission strategy of sensor nodes is defined as a combination of time-slot and transmission-offset selection. By relying on the concept of learning through interaction with the environment, the proposed protocol enhances network resilience and adaptability. In both static and mobile network scenarios, it has been compared with the state-of-the-art framed ALOHA for the underwater environment (UW-ALOHA-Q), carrier-sensing ALOHA (CS-ALOHA), and delay-aware opportunistic transmission scheduling (DOTS) protocols. The simulation results show that the proposed solution leads to significant channel utilization gains, ranging from 13% to 106% in static network scenarios and from 23% to 126% in mobile network scenarios.oreover, using a more efficient learning strategy, it significantly reduces convergence time compared to UW-ALOHA-Q in larger networks, despite the increased action space.

Analysis of Ultra-Low Frequency Oscillation in Hydro-Dominant Power System and Suppression Strategy by GPSS

After asynchronous operation, the Southwest Power Grid in China has the risk of ultra-low frequency oscillation, which seriously threatens the stable operation of the power grid. Based on the hydropower single-unit with load system, the extend Philips-Heffron model is built considering PSS/GPSS (Governor Power System Stabilizer). Then, the damping torque analysis is carried out in both prime mover and excitation side. The negative damping in the prime mover side is prominent in the ultra-low frequency band and the effect of damping ratio increase by PSS and GPSS is compared. After that, the small signal analysis of the whole system is conducted, the participation factors of the ULFO mode shows the high participation and controllability by GPSS control. At last, the proper GPSS parameters configured by small signal analysis are equipped in the hydro unit of asynchronous four-machine two-area system which reveals an obvious ULFO suppression effect. Furthermore, the practical ULFO identification algorithm based on feature points extraction and analysis which crossing the PFC (Primary Frequency Control) dead zone (49.95 - 50.05 Hz) is proposed, the GPSS control cubicle which embedded the practical ULFO identification algorithm is developed and the semi-physical simulation is conducted to verify the real ULFO suppression effect by GPSS control.

A Self-Powered Asynchronous Image Sensor With TFS Operation

This article presents a self-powered image sensor with a novel pixel architecture with energy harvesting capabilities. Pixels are autonomous entities that can harvest or sense illumination independently. Pixels have a fully asynchronous operation and do not require to be scanned to read out their asynchronous output. The sensor was manufactured in UMC 180nm technology and tested. Their specifications are competitive against the art, offering fast operation, good balance between the energy consumed and harvested, and high-dynamic range operation. The article describes the sensor and pixel architectures in detail and provides experimental results. Sensor specifications are benchmarked against the art.