Processing Nodes Research Articles

Reliability and Performance Measurement of Safety-Critical Systems Based on Petri Nets: A Case Study of Nuclear Power Plant

Safety-critical systems (SCSs) mitigate the risk of catastrophic loss of assets and hence do have high dependability targets. Performance and reliability are the critical dependability attributes, particularly in control and safety systems, and hence essential to measure to ensure the dependability. Traditional methods either are not capable to capture the system dynamics or encounter state explosion problem. Also, the methods are not able to measure all critical performance attributes. This article proposes a novel approach to measure the performance and reliability of SCSs. Such systems contain multiple interconnecting processing nodes, the functional requirements of which are modeled using Petri net (PN). A set of ordinary differential equations (ODEs) is derived from the PN model that represents the state of the system. The ODE solution can be used to measure the critical performance attributes, such as latency time and throughput of the system. The proposed method can avoid the state explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to a case study of nuclear power plant. We obtained 99.887% and 99.939% accuracy of performance and reliability measurement, respectively, which proves the effectiveness of our approach.

The Evolution of Moore’s Law Through Chipletized Architectures

Staying ahead of America’s adversaries requires control of the electromagnetic spectrum and therefore an evolution of the sensor processing chain. Agile, chip-scale, direct analog-to-digital solutions at the sensor edge are needed to overcome data bandwidth and latency limitations that are inherent with legacy EW and radar systems, and reduce design cycles, size, weight, power, and cost (SWaP-C). Heterogeneous, 2.5D integration is the next phase of Moore’s Law enabling a new approach to microelectronics based on chipletized architectures. The acceleration of deployable modular, high-performance microelectronics to the tactical edge is achieved by integrating the best silicon processing nodes for analog/digitization, processing, memory, and AI functionality on one chip. Mercury is partnering with leading semiconductor providers to deliver to defense customers the only trusted, RF system-in-package (RFSiP) devices that utilize an ecosystem of advanced, die-level building blocks, called chiplets, at the speed of innovation and purpose-built for each mission. Through assembly of high-density chiplets on a silicon interposer, on-chip die-to-die interconnects enable efficient conversion between analog and digital signals, signal processing, high-speed IO, and power management while reducing system complexity and SWaP-C. The use of open standard die-to-die interfaces allows chiplets to be rapidly upgraded or modified to meet the intended use case. In this paper, Mercury explores how 2.5D integration of state-of-the-art chiplets on a silicon interposer solves next-generation EW and radar system data processing challenges.

GFNet: A pioneering approach for precisely estimating ash content in coal through the fusion of graph convolution and feedforward network

Accurate determination of ash content in coal is paramount for quality control in the coal industry. However, conventional methods are time-consuming and ill-suited for real-time process control. To address this challenge, our study proposed a novel approach, GFNet, which integrated graph convolution and a feedforward network for ash content estimation. The image was divided into patches and transformed into a graph structure using a k-nearest Neighbor (k-NN) function. We introduced relative position encoding to each node feature to enhance feature representation. The GFNet architecture consisted of a stack of GF (graph convolution and FFN), facilitating feature extraction from the graph representation of image patches. Graph convolution captured intricate spatial relationships and interdependencies among patches, enabling effective information aggregation and updating within the graph. The FFN module, featuring two linear layers, facilitated node feature transformation. We developed pyramid (PGFNet) and isotropic (IGFNet) architectures by stacking GF blocks. Extensive experiments substantiated the superior performance of the proposed approach compared to traditional methods, emphasizing accuracy and efficiency. The GFNet achieved remarkable results, with PGFNet-S attaining a 96.36 accuracy and a mean absolute error (MAE) of 0.145%. IGFNet-Ti demonstrated a higher accuracy of 98.30% with an MAE of 0.093%. Visualization of the graph structure elucidated the model’s proficiency in processing graph structure and node features, illustrating its capability to capture content-relevant nodes and gradually connect nodes with similar semantics. This pioneering work presented a transformative approach to ash content estimation in coal quality analysis, offering an invaluable tool for the coal industry’s quality control needs.

Degree-Aware Graph Neural Network Quantization.

In this paper, we investigate the problem of graph neural network quantization. Despite the great success on convolutional neural networks, directly applying current network quantization approaches to graph neural networks faces two challenges. First, the fixed-scale parameter in the current methods cannot flexibly fit diverse tasks and network architectures. Second, the variations of node degree in a graph leads to uneven responses, limiting the accuracy of the quantizer. To address these two challenges, we introduce learnable scale parameters that can be optimized jointly with the graph networks. In addition, we propose degree-aware normalization to process nodes with different degrees. Experiments on different tasks, baselines, and datasets demonstrate the superiority of our method against previous state-of-the-art ones.

HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks

Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing. Despite numerous advantages, low computing resources at IoT devices and high communication costs for exchanging model parameters make applications of FL in massive IoT networks very limited. In this work, we develop a novel compression scheme for FL, called <i>high-compression federated learning (HCFL)</i>, for very large scale IoT networks. HCFL can reduce the data load for FL processes without changing their structure and hyperparameters. In this way, we not only can significantly reduce communication costs, but also make intensive learning processes more adaptable on low-computing resource IoT devices. Furthermore, we investigate a relationship between the number of IoT devices and the convergence level of the FL model and thereby better assess the quality of the FL process. We demonstrate our HCFL scheme in both simulations and mathematical analyses. Our proposed theoretical research can be used as a minimum level of satisfaction, proving that the FL process can achieve good performance when a determined configuration is met. Therefore, we show that HCFL is applicable in any FL-integrated networks with numerous IoT devices.

Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks

As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency.

Light in quantum computing and simulation: perspective

A summary is given of recent progress in photonic quantum simulation and computation. Non-error-corrected machines performing specialised tasks have already demonstrated a quantum advantage over the best algorithms running on conventional computers, and practical applications for such machines are being explored. Meanwhile, designs for error-corrected fault-tolerant quantum computers based on light are reducing the performance requirements for individual components and systems, although the engineering challenges are severe. Light also plays a central role in other platforms for quantum computing and simulation, from control of individual atomic qubits to remote entanglement of separate processing nodes, along with an important role in communications and other long-distance networks.

Multimode Fabry-Perot laser as a reservoir computing and extreme learning machine photonic accelerator

In this work, we introduce Fabry–Perot lasers as neuromoprhic nodes in the context of time-delayed reservoir computing and extreme learning machine (ELM) for the processing of temporal signals and the high-speed classification of images. By exploiting the multi-wavelength emission capabilities of the Fabry–Perot lasers, additional processing nodes can be introduced, thus raising the computational power without sacrificing processing speed. An experimental validation of this concept using a Fabry–Perot ELM is presented targeting a time depedent task such as channel equalization for a 50 km 28 Gbaud ‘PAM-4’ transmission, offering hard-decision forward error correction compatible performance. Additionally, the Fabry–Perot neuromorphic concept has been further strengthened by modifying the data entry technique by parallelelly assigning different samples of the input signal to different modes so as to significantly reduce speed penalty. Numerical simulations revealed that this alternative data insertion technique can offer a reduction of the processing delay and physical footprint by 75% compared to the conventional approach assigning the same symbols to all Fairy–Perot modes. Moreover, by using a similar data processing scheme in ‘MNIST’ image classification task we were able to numerically achieve a processing speed of 255.1 Mimages s−1 and a classification accuracy up to 95.95%.

Detection and utilization of new-type encrypted network traffic in distributed scenarios

Encrypted Network Traffic Classification (ENTC) is a crucial task in network management. The existing ENTC schemes usually imply two hypotheses. First, adopting a centralized processing mode. Second, the number of network traffic categories is fixed. However, it is usually unreasonable to collect all network traffic to the centralized node for processing which may cause network congestion. In addition, new traffic types emerge in endlessly, and the existing models cannot classify them. This paper studies the ENTC problem in distributed scenarios, where multiple monitoring nodes coexist, and both the existing types and new types of encrypted traffic exist at the same time. These nodes cooperate to train models, which can alleviate the challenges of limited numbers and types faced by each single node. In order to solve the proposed ENTC problem, firstly, we propose a feature extraction method in the distributed scenario. Then a detection method for new type traffic and a classification method for existing type traffic are proposed. Finally, a globally consistent automatic category labeling method and classification model updating method for new types encrypted traffic are proposed. We also take Security Information and Event Management (SIEM) as an example to discuss the managerial implications.

From field to table through the long way. Analyzing the global supply chain of Spanish tomato

Modern food chains are energy intensive processes, which implies environmental impacts and a dependence on non-renewable resources. Nevertheless, some studies tend to underestimate the energy use of transport by assuming some simplifications in their models or by not considering aggregate levels of analysis. Tomatoes are a key agricultural commodity, and Spain is the second largest tomato producer and exporter in Europe. This paper comparatively analyses the embodied energy associated with consumption products of the tomato food chain in Spain including the products consumed in the country and those exported to third countries, taking into account energy consumption from production to household distribution. We estimate the fluxes of raw materials and products of the whole chain combining official databases and business reports to reconstruct the location and interconnection between international, national and provincial nodes of production, processing and consumption. On the one hand, agrarian specialization concentrates production in provinces with greenhouse off-season production, which contributes significantly to the overall energy requirement. In addition, manufactured products have a high level of embodied energy, particularly in Spain's product consumption mix, which is the key factor in the chain, both in exports and in national food consumption. On the other hand, provincial productive and export specialization determines the existence of decoupling between agricultural, manufacturing and exporting clusters throughout the chain, significantly increasing energy use per unit consumed or exported by implying recirculation transport flows between provinces, which is particularly evident in exporting provinces supplying European countries. This process is neglected in previous LCA studies, leading to an underestimation of the energy cost. As a general conclusion, recirculation due to geographical specialization of different stages of the food chain has been shown to be a major contributor to energy use in the Spanish tomato chain and should be further studied in food chains of other products and countries. Additionally, approaches to lower food system energy use should not only focus on specific measures on separated phases of the food chain, but on reconfiguring its territorial organization and its modes of production and processing.

Thermal Crosstalk Analysis of Phase Change Memory Considering Thermoelectric Effect and Thermal Boundary Resistance

Phase change memory (PCM) has emerged as a promising memory for next-generation applications due to its high-speed read and write capabilities as well as non-volatility. However, as PCM scales down to smaller feature sizes, it faces the challenge of thermal crosstalk. During the reset operation, a large amount of heat is generated and dissipated in the PCM array, potentially affecting adjacent memory cells, compromising device stability, and limiting high-density integration. To accurately investigate the thermal crosstalk in the PCM array, the conventional finite element model of the PCM array is improved by incorporating the thermoelectric effect and thermal boundary resistance. Under the 65, 45, 32, and 22-nm process nodes, the improved model reveals the occurrence of thermal crosstalk within the PCM array, whereas the conventional model is unable to detect this phenomenon at the 65-nm node. The improved model proposed in this paper incorporates more comprehensive considerations, providing a more precise analysis of the thermal crosstalk phenomenon in the PCM array, and thereby offering theoretical reference for high-density integration of the PCM.

Root and community inference on the latent growth process of a network

Abstract Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erdős–Rényi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdős–Rényi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component can be regarded as noise. Given only one snapshot of the final network G, we study the problem of constructing confidence sets for the root node of the unobserved growth process; the root node can be patient zero in an infection network or the source of fake news in a social network. We propose inference algorithms based on Gibbs sampling that scales to networks with millions of nodes and provide theoretical analysis showing that the size of the confidence set is small if the noise level of the ER edges is not too large. We also propose variations of the model in which multiple growth processes occur simultaneously, reflecting the growth of multiple communities; we use these models to provide a new approach to community detection.

Big Data Processing over Cloud Computing Environment

The topics of this special paper are mainly devoted to the most recent research, development and applications in the field of cloud computing, big data processing in the various fields, e.g. Web blog, search engine, image processing, and industry. Among all of the submitted manuscripts, 15 papers were selected and included in this special paper. Creative thoughts and interesting inspirations are presented, discussed, and disseminated in this paper. First, there are three papers on the cloud architecture and optimization for big data processing. J. Peng, et al. discussed the problems of load balance for massive data processing on cloud considering the data processing efficiency and nodes’ capability. A load balance approach for massive data, based on the consistent hashing method, was presented to improve the data processing equalization and high processing efficiency. Large-scale sensor networks and internet of things are one of the most important application fields in big data. Y. Sun, et al. proposed a cloud computing system to support PaaS in energy power applications—PROXZONE. PROXZONE provides the cloud services uniform monitoring and authorization, and cloud message service, which can support good performance when dealing with big data. Meanwhile, S. Xu, et al. discussed an improved cooperative dynamic cluster model in multitasks mobile network, and proposed a cooperative message forwarding mechanism to improve the network performance.

Security measurement of instrumentation systems: A case study of NPP

Current instrumentation systems are distributed in nature and hence consist of different processing nodes that are interconnected with each other to exchange the data. The input data is processed into a meaningful data and output to other processing nodes. The output of the nodes depends on the input data and hence the validity of the input data needs to be ensured for the correct functioning of the overall system. Many of the instrumentation systems are safety critical in nature and contains confidential data and hence it must be secure from outside access. Due to the security significance of these systems, there is a need to assure the secrecy of systems at an early stage. In order to improve the security of instrumentation systems, this paper introduces a threat-driven modelling framework to measure the security threats using Petri Net. The technique has been validated on 21 instrumentation systems of Nuclear Power Plant and illustrated on a case study in this paper.

Influential process nodes identification strategy for aircraft assembly system based on complex network and improved PageRank

In the manufacturing network, the fluctuation of process operation time can result in the delay of the total completion time, which brings a huge challenge to controlling entire production schedule. For aircraft manufacturing industries, the processes that have a significant impact on the completion time of the assembly line need to be identified and monitored in order to ensure the final assembly efficiency and reliability. However, due to the large number of process nodes and the complexity of resource constraints and relationship constraints, traditional node centrality algorithms cannot identify influential nodes accurately. Therefore, based on complex network theory, this paper studies the influential nodes identification problem in the context of assembly lines. Firstly, The Resource-Process Coupling Network for the assembly line is constructed based on the production background and network resource characteristics. Then, based on PageRank algorithm and the idea of resource iteration, Improved PageRank Algorithm (IPRA) is proposed to identify influential process nodes, which brings the resource and time parameters into the allocation rules. Finally, according to simulation results, a comparative analysis of IPRA and existing algorithms is conducted and concludes that our method can better identify influential nodes in actual complex production networks. Furthermore, this paper identifies influential process nodes of aircraft assembly line based on the case of the commercial aircraft manufactory.

Evaluation of etching performance of single etching gases for high-κ films

The continuous advancement of CMOS technology has put forward higher requirements for dielectric etching processes. However, conventional mixed etching gases fail to elucidate the contribution of each gas. The role of single etching gas in the etching process remains elusive. In this work, we investigated the etching characteristics of high-κ thin films using a variety of single fluorine-based or chlorine-based gases. We further analyzed the underlying reasons for the observed differences in etching behaviors with different gases. Our findings demonstrate that BCl3 is the optimal single etching gas for HfO2 and Al2O3 films with a fast and stable etching rate, while the films etched by Cl2, BCl3, CF4, CHF3, and C4F8 exhibited varying degrees of etching residuals. The outstanding etching performance of BCl3 is attributed to the interaction between physical bombardment and chemical reactions. Furthermore, we propose a set of evaluation methods for etching residues and mechanisms. These results provide valuable insights into the etching process and facilitate the selection of appropriate etching gases for high-κ films in advanced semiconductor process nodes.

Distributed One-Pass Online AUC Maximization

AUC (Area Under ROC curve) is a widely used metric for measuring classification performance. Recently, due to the efficiency and scalability of online learning in handling large-scale problems, it attracts lots of interests to maximize AUC in online setting. Traditional online learning algorithms require a computation node to process all the data sequentially. However, due to the explosion in volume and velocity of modern data generated from distributed sources such as sensor network and Internet, it is not efficient to send all the data to one node for processing and updating model sequentially. To address this challenge, we explore the distributed learning techniques for maximizing AUC in online setting with a linear model. Specifically, we propose two different algorithms for solving the distributed online AUC maximization problem: (i) the Centralized Distributed One-Pass Online AUC Maximization (C-DOPOAM) algorithm in which the server updates the global model with the gradients computed from workers; and (ii) the Decentralized Distributed One-Pass Online AUC Maximization (D-DOPOAM) algorithm in which each worker updates its local model and exchanges information with its neighbors to achieve consensus. Moreover, we establish the regret bounds of the two proposed algorithms. They achieve the similar convergence rate with the state-of-the-art non-distributed online AUC maximization algorithms. Finally, we conduct extensive experiments to validate our theoretical result on different computation platforms and network configurations. We also make a comparison between C-DOPOAM and D-DOPOAM in poor network condition. The results show D-DOPOAM is more robust than C-DOPOAM against low bandwidth or high latency.

Multi-material heterogeneous integration on a 3-D photonic-CMOS platform

Photonics has been one of the primary beneficiaries of advanced silicon manufacturing. By leveraging mature complementary metal-oxide-semiconductor (CMOS) process nodes, unprecedented device uniformity and scalability have been achieved at low costs. However, some functionalities, such as optical memory, Pockels modulation, and magnetooptical activity, are challenging or impossible to acquire on group-IV materials alone. Heterogeneous integration promises to expand the range of capabilities within silicon photonics. Existing heterogeneous integration protocols are nonetheless not compatible with active silicon processes offered at most photonic foundries. In this work, we propose a novel heterogeneous integration platform that will enable wafer-scale, multi-material integration with active silicon-based photonics, requiring zero change to the existing foundry process. Furthermore, the platform will also pave the way for a class of high-performance devices. We propose a grating coupler design with peak coupling efficiency reaching 93%, an antenna with peak diffraction efficiency in excess of 97%, and a broadband adiabatic polarization rotator with conversion efficiency exceeding 99%.

Study on the Circuit Performance of Various Interconnect Metal Materials in the Latest Process Nodes

Study on the Circuit Performance of Various Interconnect Metal Materials in the Latest Process Nodes

Demonstration of optically-assisted reconfigurable average of two 20-Gbaud 4-phase-encoded data channels using nonlinear wave mixing.

Networks can play a key role in high-speed and reconfigurable arithmetic computing. However, two performance bottlenecks may arise when: (i) relying solely on electronics to handle computation for multiple data channels at high data rates, and (ii) the data streams input to a processing node (PN) are transmitted as phase-encoded signals over an optical network. We experimentally demonstrate the operation of optically-assisted reconfigurable average of two 4-phase-encoded data channels at 10- and 20-Gbaud rates. Our input signals are two streams of 2-bit numbers representing a binary floating-point format, and the operation results in 7-phase-encoded output signals represented by 3-bit numbers. The average operation is achieved in three stages: (1) phase encoding and division-using an optical modulator to encode the data streams; (2) summation-using a highly nonlinear fiber (HNLF); and (3) multicast-using a periodically poled lithium niobate (PPLN) waveguide to multicast back the result into the original signal wavelengths. The experimental results validate the concept, and the measured penalties indicate that: (i) the error vector magnitudes (EVMs) of optical signals increase at each stage and reach ∼18-21% for the final multicast results, and (ii) compared to the inputs, the optical signal-to-noise ratio (OSNR) penalty of output is ∼6.7 dB for the 10-Gbaud rate and ∼6.9 dB for the 20-Gbaud rate at a bit error rate (BER) of 3.8e-3.