Latency Problem Research Articles

IDSNN: Towards High-Performance and Low-Latency SNN Training via Initialization and Distillation.

Spiking neural networks (SNNs) are widely recognized for their biomimetic and efficient computing features. They utilize spikes to encode and transmit information. Despite the many advantages of SNNs, they suffer from the problems of low accuracy and large inference latency, which are, respectively, caused by the direct training and conversion from artificial neural network (ANN) training methods. Aiming to address these limitations, we propose a novel training pipeline (called IDSNN) based on parameter initialization and knowledge distillation, using ANN as a parameter source and teacher. IDSNN maximizes the knowledge extracted from ANNs and achieves competitive top-1 accuracy for CIFAR10 (94.22%) and CIFAR100 (75.41%) with low latency. More importantly, it can achieve 14× faster convergence speed than directly training SNNs under limited training resources, which demonstrates its practical value in applications.

A Low-Latency, Low-Jitter Retimer Circuit for PCIe 6.0

As the PCIe 6.0 specification places higher requirements on signal integrity and transmission latency, it becomes especially important to improve signal transmission performance at the physical layer of the transceiver interface. Retimer circuits are a key component of high-speed serial interfaces, and their delay and jitter size directly affect the overall performance of PCIe. For the typical retimer circuit with large-latency and low-jitter performance, this paper proposes a low-latency and low-jitter Retimer circuit based on CDR + PLL architecture for PCIe 6.0, using a jitter-canceling filter circuit to eliminate the frequency difference between the retiming clock and data, reduce the retiming clock jitter, and improve the quality of Retimer output data. The data are sampled using the retiming clock and then output, avoiding the problem of large penetration latency of typical retimer circuits. The circuit is designed using the CMOS 28 nm process. Simulation results show that when 112 Gbps PAM4 data are input to the retimer circuit, the Retimer penetration latency is 27.3 ps, which is 83.5% lower than the typical Retimer structure; the output jitter data are 741 fs, a 31.4% reduction compared to the typical retimer structure.

Turbo encoder and decoder chip design and FPGA device analysis for communication system

<p>Turbo codes are error-correcting codes with performance that is close to the Shannon theoretical limit (SHA). The motivation for using turbo codes is that the codes are an appealing mix of a random appearance on the channel and a physically realizable decoding structure. The communication systems have the problem of latency, fast switching, and reliable data transfer. The objective of the research paper is to design and turbo encoder and decoder hardware chip and analyze its performance. Two convolutional codes are concatenated concurrently and detached by an interleaver or permuter in the turbo encoder. The expected data from the channel is interpreted iteratively using the two related decoders. The soft (probabilistic) data about an individual bit of the decoded structure is passed in each cycle from one elementary decoder to the next, and this information is updated regularly. The performance of the chip is also verified using the maximum a posteriori (MAP) method in the decoder chip. The performance of field-programmable gate array (FPGA) hardware is evaluated using hardware and timing parameters extracted from Xilinx ISE 14.7. The parallel concatenation offers a better global rate for the same component code performance, and reduced delay, low hardware complexity, and higher frequency support.</p>

WALTZ: Leveraging Zone Append to Tighten the Tail Latency of LSM Tree on ZNS SSD

We propose WALTZ, an LSM tree-based key-value store on the emerging Zoned Namespace (ZNS) SSD. The key contribution of WALTZ is to leverage the zone append command, which is a recent addition to ZNS SSD specifications, to provide tight tail latency. The long tail latency problem caused by the merging process of multiple parallel writes, called batch-group writes, is effectively addressed by the internal synchronization mechanism of ZNS SSD. To provide fast failover when the active zone becomes full for a write-ahead log (WAL) file during parallel append, WALTZ introduces a mechanism for WAL zone replacement and reservation. Finally, lazy metadata management allows a put query to be processed fast without requiring any other synchronizations to enable lock-free execution of individual append commands. For evaluation we use both mi-crobenchmarks (db_bench) with varying read/write ratios and key skewnesses, and realistic social-graph workloads (MixGraph from Facebook). Our evaluation demonstrates geomean reduction of tail latency by 2.19× and 2.45× for db_bench and MixGraph, respectively, with a maximum reduction of 3.02× and 4.73×. As a side effect of eliminating the overhead of batch-group writes, WALTZ also improves the query throughput (QPS) by up to 11.7%.

The latency of domestic violence

The article analyzes the problem of latency of domestic violence. Violence in the family is one of the most acute problems of our time, which has deep root and latent character, has no age, social and state boundaries. The concepts of «latency» and «domestic violence» are considered and analyzed, and the scientific views of researchers regarding these concepts are presented. The types and features of violence that can manifest in the family, namely physical, mental, sexual and economic, are revealed.The reasons that contribute to the silence of domestic violence have been identified and revealed. These reasons include internal and external factors. In their work, the authors also analyze the statistics of domestic violence in recent years. The legal basis of the researched issue is analyzed. It was determined that domestic violence is sanctioned in accordance with the Criminal Code of Ukraine. Directions for the prevention of the latency of domestic crime are proposed. The article concludes that the problem of family violence does not lose its relevance and is recognized as one of the most acute social problemsof Ukrainian society.It is indicated that a successful theoretical and practical solution to such important criminological problems as identifying the causes and conditions of crime, as well as effective counteraction to it, is possible if there is an objective consideration of allcrimes committed in society. Domestic violence is recognized as one of the most acute social problems of Ukrainian society, and therefore does not lose its relevance today and Latent crime in the field of domestic violencе is a set of criminal acts that took place in a certain family, but were not recorded by law enforcement agencies and were not reflected in official statistical data. To date, the increase in the number of complaints about domestic violence is not a worsening of the situation in the region or inUkraine, but is primarily a decrease in its latency, due to a change in people’s attitude to this problem and its greater disclosure.

Optimization and Improvement of BP Decoding Algorithm for Polar Codes Based on Deep Learning

Abstract In order to solve the high latency problem of polar codes belief propagation decoding algorithm in the 5G and the dimension limitation problem of belief propagation decoding algorithm under deep learning, a multilayer perceptron belief propagation decoding (MLP-BP) algorithm based on partitioning idea is proposed. In this work, polar codes is decoded using neural networks in partitioning, and the right transfer message value of BP decoding algorithm is also set to complete the propagation process. Simulation results show that, compared with BP decoding algorithm, the proposed algorithm has better decoding performance, reducing the decoding latency, and it is also applicable to long polar codes.

Wideband High-Speed and High-Accuracy Instantaneous Frequency Measurement System

A new photonic-assisted instantaneous frequency measurement system is presented. It overcomes the latency problem in the reported structures based on the frequency-to-time mapping technique or the frequency-to-power mapping technique that involves a long length of fiber, and at the same time, enables the incoming microwave signal frequency to be measured over a wide frequency range with only small errors. The system generates three low-frequency signals. The phases of the three low-frequency signals are compared. One of the two low-frequency signal phase differences is used to estimate the incoming microwave signal frequency unambiguously over a wide frequency range and the other is used to provide accurate microwave signal frequency measurement. A proof-of-concept experiment is set up. Experimental results show, by measuring the phase difference of two low-frequency signals, the frequency of the input microwave signal can be determined unambiguously in 15 GHz and 500 MHz frequency ranges with errors below ±220 MHz and ±10 MHz respectively. Hence, by using two low-frequency signal phase differences, the input microwave signal frequency can be determined accurately over a wide frequency range. The new photonic-assisted frequency measurement system has a fast response time, which is an order of magnitude shorter than that of the systems based on the frequency-to-time mapping technique and the frequency-to-power mapping technique with a kilometer-long fiber.

PRESCAN Adaptive Vehicle Image Real-Time Stitching Algorithm Based on Improved SIFT

Aiming at the problems of high latency, low accuracy, and obvious stitching gaps of traditional image stitching techniques in PRESCAN, this paper proposes an adaptive real-time vehicle image stitching algorithm based on improved scale-invariant feature transform (SIFT). Firstly, a multi-threaded structure is utilized to combine the improved adaptive features from accelerated segment test algorithm with SIFT descriptors for feature extraction. Secondly, a mismatching filtering strategy based on the BF algorithm has been introduced. Finally, image stitching is performed by combining random sample consensus algorithm and adaptive region stitching strategy. The results show that the recall rate is about 30.64%, the accuracy of matching is about 98.86%, and the total stitching time is about 22.82ms. It effectively improves the quality of feature point extraction and matching accuracy and significantly reduces the stitching time of images. It provides a real-time and accurate vehicle image stitching method for the field of perception in the autonomous driving laboratory environment.

An Improved Spectrum Trading Design Based on Dynamic Credit Aggregate-Signature Blockchain

At present, the number of incoming devices and mobile data traffic has seen explosive growth, and spectrum resources are difficult to meet the increasing demand. The traditional centralized spectrum management model suffers from low spectrum utilization, high maintenance costs, and data security issues. The decentralized and secure nature of blockchain technology is a natural advantage in solving the problems faced by the current spectrum management model. However, when introducing traditional blockchain technology into spectrum management, it faces problems such as high latency and high resource consumption. In this letter, we design a new blockchain-enabled spectrum trading framework consisting of the access layer, distribution layer, and core layer, and then propose a user benefit-maximum spectrum trading matching scheme. To solve the problem of latency and energy consumption of consensus algorithms in blockchains, the dynamic credit aggregate-signature Byzantine fault tolerance (DCABFT) algorithm based on the Boneh-Lynn-Shacham (BLS) aggregated signature is proposed. Simulation results demonstrate that the proposed scheme can greatly improve the transaction latency and users’ quality of service.

Moving Microgrid Hierarchical Control to an SDN-Based Kubernetes Cluster: A Framework for Reliable and Flexible Energy Distribution

Software Defined Networking (SDN) is a communication alternative to increase the scalability and resilience of microgrid hierarchical control. The common architecture has a centralized and monolithic topology, where the controller is highly susceptible to latency problems, resiliency, and scalability issues. This paper proposes a novel and intelligent control network to improve the performance of microgrid communications, solving the typical drawback of monolithic SDN controllers. The SDN controller's functionalities are segregated into microservices groups and distributed through a bare-metal Kubernetes cluster. Results are presented from PLECS hardware in the loop simulation to validate the seamless transition between standard hierarchical control to the SDN networked microgrid. The microservices significantly impact the performance of the SDN controller, decreasing the latency by 10.76% compared with a monolithic architecture. Furthermore, the proposed approach demonstrates a 42.23% decrease in packet loss versus monolithic topologies and a 53.41% reduction in recovery time during failures. Combining Kubernetes with SDN microservices can eliminate the single point of failure in hierarchical control, improve application recovery time, and enhance containerization benefits, including security and portability. This proposal represents a reference framework for future edge computing and intelligent control approaches in networked microgrids.

Deep-Reinforcement-Learning-Based Resource Allocation for Cloud Gaming via Edge Computing

Compared with cloud computing, edge computing is capable of effectively solving the high latency problem in cloud gaming. However, there are still several challenges to address for optimizing system performance. On the one hand, the unpredictable bursts of game requests can cause server overload and network congestion. On the other hand, the mobility of players makes the system highly dynamic. Although existing research has studied game fairness and latency separately to improve the Quality of Experience (QoE), a tradeoff between fairness and latency has been largely ignored. Furthermore, how to balance network and computing load is identified as another constraint during optimization. Focusing on latency, fairness, and load balance simultaneously, we propose an adaptive resource allocation strategy through deep reinforcement learning (DRL) for a dynamic gaming system. The experimental results have demonstrated that the proposed algorithm outperforms the traditional optimization methods and classical reinforcement learning algorithms in solving complex multimodal reward problems.

Trainable quantization for Speedy Spiking Neural Networks.

Spiking neural networks are considered as the third generation of Artificial Neural Networks. SNNs perform computation using neurons and synapses that communicate using binary and asynchronous signals known as spikes. They have attracted significant research interest over the last years since their computing paradigm allows theoretically sparse and low-power operations. This hypothetical gain, used from the beginning of the neuromorphic research, was however limited by three main factors: the absence of an efficient learning rule competing with the one of classical deep learning, the lack of mature learning framework, and an important data processing latency finally generating energy overhead. While the first two limitations have recently been addressed in the literature, the major problem of latency is not solved yet. Indeed, information is not exchanged instantaneously between spiking neurons but gradually builds up over time as spikes are generated and propagated through the network. This paper focuses on quantization error, one of the main consequence of the SNN discrete representation of information. We argue that the quantization error is the main source of accuracy drop between ANN and SNN. In this article we propose an in-depth characterization of SNN quantization noise. We then propose a end-to-end direct learning approach based on a new trainable spiking neural model. This model allows adapting the threshold of neurons during training and implements efficient quantization strategies. This novel approach better explains the global behavior of SNNs and minimizes the quantization noise during training. The resulting SNN can be trained over a limited amount of timesteps, reducing latency, while beating state of the art accuracy and preserving high sparsity on the main datasets considered in the neuromorphic community.

Highly Reliable Ovonic Threshold Switch with TiN/GeTe/TiN Structure

A new architecture has become necessary owing to the power consumption and latency problems of the von Neumann architecture. A neuromorphic memory system is a promising candidate for the new system as it has the potential to process large amounts of digital information. A crossbar array (CA), which consists of a selector and a resistor, is the basic building block for the new system. Despite the excellent prospects of crossbar arrays, the biggest obstacle for them is sneak current, which can cause a misreading between the adjacent memory cells, thus resulting in a misoperation in the arrays. The chalcogenide-based ovonic threshold switch (OTS) is a powerful selector with highly nonlinear I-V characteristics that can be used to address the sneak current problem. In this study, we evaluated the electrical characteristics of an OTS with a TiN/GeTe/TiN structure. This device shows nonlinear DC I-V characteristics, an excellent endurance of up to 109 in the burst read measurement, and a stable threshold voltage below 15 mV/dec. In addition, at temperatures below 300 °C, the device exhibits good thermal stability and retains an amorphous structure, which is a strong indication of the aforementioned electrical characteristics.

Low-Latency SCL Polar Decoder Architecture Using Overlapped Pruning Operations

Allowing the superior error-correction performance even for short-length codewords, the successive-cancellation list (SCL) decoding algorithm has allowed the polar code to be adopted in 5G New Radio standard for control channel. However, existing SCL polar decoders still suffer from long processing latency caused by a number of serialized internal operations. In this work, to solve the latency problem, we present several parallel computing solutions for the serialized operations, i.e., simplified data dependencies and two overlapped pruning operations. To realize the proposed parallel computing, we also introduce internal circuit blocks including dual read-port buffers, an on-the-fly parity checker, and overlapped processing units. The proposed SCL polar decoders are precisely designed with optimal design parameters by analyzing trade-offs between the latency reduction and area overheads. Implemented in a 65-nm CMOS technology, the proposed list-8 SCL polar decoder requires only 374 ns to handle a (1024, 512) 5G codeword, improving the decoding efficiency by 34.7% compared to the previous designs.

Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing

Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be processed without latency for safety. Indoor autonomous driving is attracting attention as one of the mobile edge computing services. Furthermore, it relies on its sensors for location recognition because indoor autonomous driving cannot use a GPS device, as is the case with outdoor driving. However, while the autonomous vehicle is being driven, the real-time processing of external events and the correction of errors are required for safety. Furthermore, an efficient autonomous driving system is required because it is a mobile environment with resource constraints. This study proposes neural network models as a machine-learning method for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the range data measured with the LiDAR sensor. We designed six neural network models to be evaluated according to the number of input data points. In addition, we made an autonomous vehicle based on the Raspberry Pi for driving and learning and an indoor circular driving track for collecting data and performance evaluation. Finally, we evaluated six neural network models in terms of confusion matrix, response time, battery consumption, and driving command accuracy. In addition, when neural network learning was applied, the effect of the number of inputs was confirmed in the usage of resources. The result will influence the choice of an appropriate neural network model for an indoor autonomous vehicle.

Microservice combination optimisation based on improved gray wolf algorithm

Microservices architecture is a new paradigm for application development. The problem of optimising the performance of microservice architectures from a non-functional perspective is a typical Nondeterministic Polynomial (NP) problem. Therefore, aiming to quantify the non-functional requirements of computing microservice systems, while solving the problem of latency in computing the best combination of services with the maximum QoS objective function value, this paper proposes a microservice combination approach based on the QoS model and a CGWO algorithm for optimisation computation for this model. The experimental results verify that the error rate of the method is only 0.528% on the non-functional combination optimisation problem, and the computational efficiency of the algorithm increases by 97.29% when the complexity of the problem search space increases, while CGWO improves 65.97% and 81.25% respectively in the accuracy of optimisation compared to the prototype of the algorithm (GWO), and has a stable optimisation performance, aspect. It proves that the research in this paper has a high advantage in automatically searching for the best QoS for the microservice combination problem.

P-PBFT: An improved blockchain algorithm to support large-scale pharmaceutical traceability

To solve the problems of high latency, high system overhead, and small supported scale in the current application of pharmaceutical traceability combined with blockchain technology, an algorithm called Pharmaceutical-Practical Byzantine Fault Tolerance (P-PBFT) based on PBFT, grouping, and credit voting is proposed. The algorithm combines the characteristics of a pharmaceutical supply chain, optimizes the consistency protocol in the original algorithm, divides large-scale network nodes into different consensus sets by response speed, and performs grouping consensus. The algorithm's credit model and voting mechanism dynamically updates user status according to the behavior of nodes in consensus, evaluates the reliability of users, and also serves as a basis for electing management nodes. Experimental results show that the improved P-PBFT consensus algorithm provides smaller latency and higher throughput for pharmaceutical traceability systems, supports larger-scale traceability, effectively alleviates the dramatic increase in communication among network nodes, and reduces the influence of malicious nodes.

Managing the cyber-physical security for unmanned aerial vehicles used in perimeter surveillance

This paper discusses the vulnerabilities of perimeter surveillance unmanned aerial vehicles to cyber-physical security threats and discusses some approaches to manage them, as most cyber threats to the UAVs coming through their onboard wireless transceiver, we are suggesting an Antennas propagation type that limit the vector of the threat, also the importance of vulnerabilities scanner to evaluate the system risks. And addressing the limited energy and computation power resources onboard, a computation efficient onboard encryption method is proposed, and a sign cutting machine vision algorithm to provide warning of suspicious activity detected on interrupted surveillance imagery. The focus of this analysis is to manage the vulnerabilities of the system during both its operation time and its standby time, this is done by performing a checklist of computerized tests periodically and comparing two or more results to discover any unexpected changes. We showed that the onboard resources can be utilized more efficiently securing itself and the system from possible intrusion, however, some heavy calculation tasks still need to be performed on the ground control station which is causing some latency problem to such time sensitive operation. We have demonstrated that image registration techniques have produced useful results when applied to analyze the differences between two scene images.

Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint.

In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices.

Advanced DAG-Based Ranking (ADR) Protocol for Blockchain Scalability

In the past decade, blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention. However, current blockchain systems face the problems of limited throughput, poor scalability, and high latency. Due to the failure of consensus algorithms in managing nodes’identities, blockchain technology is considered inappropriate for many applications, e.g., in IoT environments, because of poor scalability. This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking (ADR) protocol to improve blockchain scalability and throughput. The ADR protocol uses the directed acyclic graph ledger, where nodes are placed according to their ranking positions in the graph. It allows honest nodes to use the Direct Acyclic Graph (DAG) topology to write blocks and verify transactions instead of a chain of blocks. By using a three-step strategy, this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability. The first step involves the safe entry of nodes into the system by verifying their private and public keys. The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions. In the third step, a ranking algorithm is developed to separate the nodes created by attackers. After eliminating attacker nodes, the nodes are ranked according to their performance in the system, and true nodes are arranged in blocks in topological order. As a result, the ADR protocol is suitable for applications in the Internet of Things (IoT). We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes. Based on the simulation results, this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications (IOTA) and ByteBall.