Extra Network Research Articles

Learning Lightweight Dynamic Kernels With Attention Inside via Local-Global Context Fusion.

Traditional convolutional neural networks (CNNs) share their kernels among all positions of the input, which may constrain the representation ability in feature extraction. Dynamic convolution proposes to generate different kernels for different inputs to improve the model capacity. However, the total parameters of the dynamic network can be significantly huge. In this article, we propose a lightweight dynamic convolution method to strengthen traditional CNNs with an affordable increase of total parameters and multiply-adds. Instead of generating the whole kernels directly or combining several static kernels, we choose to "look inside," learning the attention within convolutional kernels. An extra network is used to adjust the weights of kernels for every feature aggregation operation. By combining local and global contexts, the proposed approach can capture the variance among different samples, the variance in different positions of the feature maps, and the variance in different positions inside sliding windows. With a minor increase in the number of model parameters, remarkable improvements in image classification on CIFAR and ImageNet with multiple backbones have been obtained. Experiments on object detection also verify the effectiveness of the proposed method.

PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance

Variational Autoencoder based Bayesian Optimization (VAE-BO) has demonstrated its excellent performance in addressing high-dimensional structured optimization problems. However, current mainstream methods overlook the potential of utilizing a pool of unlabeled data to construct the latent space, while only concentrating on designing sophisticated models to leverage the labeled data. Despite their effective usage of labeled data, these methods often require extra network structures, additional procedure, resulting in computational inefficiency. To address this issue, we propose a novel method to effectively utilize unlabeled data with the guidance of labeled data. Specifically, we tailor the pseudo-labeling technique from semi-supervised learning to explicitly reveal the relative magnitudes of optimization objective values hidden within the unlabeled data. Based on this technique, we assign appropriate training weights to unlabeled data to enhance the construction of a discriminative latent space. Furthermore, we treat the VAE encoder and the Gaussian Process (GP) in Bayesian optimization as a unified deep kernel learning process, allowing the direct utilization of labeled data, which we term as Gaussian Process guidance. This directly and effectively integrates the goal of improving GP accuracy into the VAE training, thereby guiding the construction of the latent space. The extensive experiments demonstrate that our proposed method outperforms existing VAE-BO algorithms in various optimization scenarios. Our code will be published at https://github.com/TaicaiChen/PG-LBO.

Target-aware transformer tracking with hard occlusion instance generation.

Visual tracking is a crucial task in computer vision that has been applied in diverse fields. Recently, transformer architecture has been widely applied in visual tracking and has become a mainstream framework instead of the Siamese structure. Although transformer-based trackers have demonstrated remarkable accuracy in general circumstances, their performance in occluded scenes remains unsatisfactory. This is primarily due to their inability to recognize incomplete target appearance information when the target is occluded. To address this issue, we propose a novel transformer tracking approach referred to as TATT, which integrates a target-aware transformer network and a hard occlusion instance generation module. The target-aware transformer network utilizes an encoder-decoder structure to facilitate interaction between template and search features, extracting target information in the template feature to enhance the unoccluded parts of the target in the search features. It can directly predict the boundary between the target region and the background to generate tracking results. The hard occlusion instance generation module employs multiple image similarity calculation methods to select an image pitch in video sequences that is most similar to the target and generate an occlusion instance mimicking real scenes without adding an extra network. Experiments on five benchmarks, including LaSOT, TrackingNet, Got10k, OTB100, and UAV123, demonstrate that our tracker achieves promising performance while running at approximately 41 fps on GPU. Specifically, our tracker achieves the highest AUC scores of 65.5 and 61.2% in partial and full occlusion evaluations on LaSOT, respectively.

Deep temporal clustering features for speech emotion recognition

Deep clustering is a popular unsupervised technique for feature representation learning. We recently proposed the chunk-based DeepEmoCluster framework for speech emotion recognition (SER) to adopt the concept of deep clustering as a novel semi-supervised learning (SSL) framework, which achieved improved recognition performances over conventional reconstruction-based approaches. However, the vanilla DeepEmoCluster lacks critical sentence-level temporal information that is useful for SER tasks. This study builds upon the DeepEmoCluster framework, creating a powerful SSL approach that leverages temporal information within a sentence. We propose two sentence-level temporal modeling alternatives using either the temporal-net or the triplet loss function, resulting in a novel temporal-enhanced DeepEmoCluster framework to capture essential temporal information. The key contribution to achieving this goal is the proposed sentence-level uniform sampling strategy, which preserves the original temporal order of the data for the clustering process. An extra network module (e.g., gated recurrent unit) is utilized for the temporal-net option to encode temporal information across the data chunks. Alternatively, we can impose additional temporal constraints by using the triplet loss function while training the DeepEmoCluster framework, which does not increase model complexity. Our experimental results based on the MSP-Podcast corpus demonstrate that the proposed temporal-enhanced framework significantly outperforms the vanilla DeepEmoCluster framework and other existing SSL approaches in regression tasks for the emotional attributes arousal, dominance, and valence. The improvements are observed in fully-supervised learning or SSL implementations. Further analyses validate the effectiveness of the proposed temporal modeling, showing (1) high temporal consistency in the cluster assignment, and (2) well-separated emotional patterns in the generated clusters.

(Invited) Opening Twisted Molecular Chains of High Solid Content Screen Printable Ink for Screen-Printed Low-Tortuosity Electrodes

Reducing the tortuosity of electrodes is an efficient technique for enhancing the charge transfer inside electrodes and boosting the fast-charging capabilities of lithium-ion batteries for electric vehicles. Compared to reported complex methods, screen printing is facile, economical, and printing pattern controllable. More importantly, the roll-to-roll screen printing equipment can be integrated into the existing electrode production system, allowing for the industrialization of low-tortuosity electrodes. However, the absence of screen-printable inks with a high solid content restricts the ink transfer volume per printing, lowering production efficiency and raising the price of screen-printed electrodes. By integrating rheological characteristics analysis, X-ray computed tomography characterization, and coarse-grained molecular dynamics (CGMD) simulation, we are able to recognize and illustrate the relevance of the molecular chain state in high-solid-content ink for screen printability. Additionally, we propose and develop a unique preparation procedure for producing high-solid-content screen-printable inks. In addition, the electrochemical performance of screen-printed electrodes was evaluated in order to enclose the importance of electrode micromorphology. This finding lays the door for the industrialization of screen-printable inks with high solid content and encourages additional research into screen-printed architectures with functional electrodes.In this work, we demonstrated for the first time that the molecular chains within high-solid-content inks should be untangled to obtain outstanding screen printability. Specifically, the twisted molecular chains serve as extra networks that drag certain particles within the electrode ink onto the mesh, hence diminishing its screen printability. Therefore, we suggest a two-step approach for opening the twisted molecular chains of inks with a high solid content of 60%. Employing this type of ink, the mass loading per printing layer elevates to about 5 times greater than the existing cathode inks. After opening the twisted chains, the LiNi0.6Mn0.2Co0.2O2 cathode ink exhibited a lower viscosity, a faster thixotropic recovery, and superior screen printability than the ink containing twisted chains. Furthermore, due to the improved internal architectural integrity of screen-printed electrodes printed by the excellent screen-printability of ink, the electrode printed with optimized inks displayed a 33% larger charge capacity at 6C with a mass loading of 6.5 mg/cm2 than that produced with chains-twisted ink. In addition, based on the fundamental of the two-step approach, we present optimization approaches for altering screen-printable inks for a variety of circumstances. This idea could promote the coupling of screen printing with the production of functional electrodes and could be utilized for the production of screen-printable inks for all fields.

Fast One-Sided RDMA-Based State Machine Replication for Disaggregated Memory

Disaggregated memory architecture has risen in popularity for large datacenters with the advantage of improved resource utilization, failure isolation, and elasticity. Replicated state machines (RSMs) have been extensively used for reliability and consistency. In traditional RSM protocols, each replica stores replicated data and has the computing power to participate in some part of the protocols. However, traditional RSM protocols fail to work in the disaggregated memory architecture due to asymmetric resources on CPU nodes and memory nodes. This article proposes ECHO, a fast one-sided RDMA-based RSM protocol with lightweight log replication and remote applying, efficient linearizability guarantee, and fast coordinator failure recovery. ECHO enables all operations in the protocol to be efficiently executed using only one-sided RDMA, without the participation of any computing resource in the memory pool. To provide lightweight log replication and remote applying, ECHO couples the replicated log and the state machine to avoid dual-copy and performs remote applying by updating pointers. To enable efficient remote log state management, ECHO leverages a hitchhiked log state updating scheme to eliminate extra network round trips. To provide efficient linearizability guarantee, ECHO performs immediate remote applying after log replication and leverages the local locks at the coordinator to ensure linear consistency. Moreover, ECHO adopts a commit-aware log cache to make data visible immediately after being committed. To achieve fast failure recovery, ECHO leverages a commit point identification scheme to reduce the overhead of log consistency recovery. Experimental results demonstrate that ECHO outperforms the state-of-the-art RSM protocol (namely Sift) in multiple scenarios. For example, ECHO achieves 27%–52% higher throughput on typical write-intensive workloads. Moreover, ECHO reduces the consistency recovery time by three orders of magnitude for coordinator failure.

Real-Time Stereo 3D Car Detection With Shape-Aware Non-Uniform Sampling

Pseudo-LiDAR based stereo 3D detectors have gained popularity due to their high accuracy. However, these methods need dense depth supervision and suffer from inferior speed. To solve these two issues, a recently introduced RTS3D builds an efficient 4D feature-consistency embedding (FCE) space for the object intermediate representation without depth supervision, where FCE space performs uniform sampling to generate feature sampling points, which ignores the importance of different object regions. In this paper, we observe that, compared with the inner region, the outer region of the object plays a more important role for accurate 3D detection. To fully exploit the useful information from the outer region, we propose a novel shape-aware non-uniform sampling strategy. Instead of uniform sampling, our proposed non-uniform sampling strategy performs dense sampling in outer region and sparse sampling in inner region. Therefore, more points are sampled from the outer region and more useful features are extracted for 3D detection. In addition, we design a high-level semantic enhanced FCE module to exploit more contextual information and suppress noise better. As a result, it further improves feature discrimination of each sampling point. Experimental results on the KITTI dataset show the effectiveness of the proposed method. Compared with the baseline RTS3D, our proposed method has 2.57% improvement on car AP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{3d}$ </tex-math></inline-formula> almost without extra network parameters. Moreover, our proposed method outperforms the state-of-the-art methods without extra supervision at a real-time speed.

Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection

The detection of vibration dampers is important for power systems. Deep learning-based damper detection needs massive annotations, which are labour-intensive and time-consuming. Therefore, weakly supervised object detection is considered. Part domination is the main problem in weakly supervised vibration damper detection. Current weakly supervised object detection methods neglect that overwhelming negative instances exist in each image during the training phase, which would mislead the training and make detection results stuck in the most discriminative parts of objects. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module, combining random sampling and IoU-balanced sampling, progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances to make the network focus on positive instances. Extensive experimental results on the vibration damper, PASCAL VOC 2007, and PASCAL VOC 2012 datasets demonstrate that the proposed method can significantly improve the baseline, which is also comparable to many existing methods. In addition, compared to the baseline, the proposed method requires no extra network parameters, and the supplementary training overheads are small.

On Scalable Network Communication for Infrastructure-Vehicle Collaborative Autonomous Driving

Network communication is crucial for Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD), which can help to improve both driving safety and efficiency. Existing IVCAD systems either use RoadSide Units (RSUs) deployed sparsely in critical locations to extend the on-board sensing, or use RSUs deployed densely in a limited area to provide both sensing and decision-making for vehicles. For both scenarios, the numbers of RSUs and vehicles are small, and the network communication solutions are based on the existing wireline and cellular network infrastructures and their extensions. However, it remains unknown whether these existing network communication solutions are scalable when the IVCAD system is deployed city-widely with hundreds of thousands of RSUs and millions of vehicles. In this paper, we analyze the network communication requirements for different types of IVCAD systems, and show that when the numbers of RSUs and vehicles in the system are large, using the existing solutions to support IVCAD can incur very high extra network communication facility cost. We further discuss how to use wireless mesh networks formed by RSUs themselves to solve the network communication problem without any deployment of extra network communication facilities. To enable the RSU network, we provide a network communication framework based on network coding and highlight some research directions.

Variable Selection of Spatial Logistic Autoregressive Model with Linear Constraints.

In recent years, spatial data widely exist in various fields such as finance, geology, environment, and natural science. These data collected by many scholars often have geographical characteristics. The spatial autoregressive model is a general method to describe the spatial correlations among observation units in spatial econometrics. The spatial logistic autoregressive model augments the conventional logistic regression model with an extra network structure when the spatial response variables are discrete, which enhances classification precision. In many application fields, prior knowledge can be formulated as constraints on the parameters to improve the effectiveness of variable selection and estimation. This paper proposes a variable selection method with linear constraints for the high-dimensional spatial logistic autoregressive model in order to integrate the prior information into the model selection. Monte Carlo experiments are provided to analyze the performance of our proposed method under finite samples. The results show that the method can effectively screen out insignificant variables and give the corresponding coefficient estimates of significant variables simultaneously. As an empirical illustration, we apply our method to land area data.

A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud–Fog Computing

Cloud–fog computing is a large-scale service environment developed to deliver fast, scalable services to clients. The fog nodes of such environments are distributed in diverse places and operate independently by deciding on which data to process locally and which data to send remotely to the cloud for further analysis, in which a Service-Level Agreement (SLA) is employed to govern Quality of Service (QoS) requirements of the cloud provider to such nodes. The provider experiences varying incoming workloads that come from heterogeneous fog and Internet of Things (IoT) devices, each of which submits jobs that entail various service characteristics and QoS requirements. To execute fog workloads and meet their SLA obligations, the provider allocates appropriate resources and utilizes load scheduling strategies that effectively manage the executions of fog jobs on cloud resources. Failing to fulfill such demands causes extra network bottlenecks, service delays, and energy constraints that are difficult to maintain at run-time. This paper proposes a joint energy- and QoS-optimized performance framework that tolerates delay and energy risks on the cost performance of the cloud provider. The framework employs scheduling mechanisms that consider the SLA penalty and energy impacts of data communication, service, and waiting performance metrics on cost reduction. The findings prove the framework’s effectiveness in mitigating energy consumption due to QoS penalties and therefore reducing the gross scheduling cost.

Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network

A heterogeneous vehicular network (HetVNET) is a promising network architecture that combines multiple network technologies such as IEEE 802.11p, dedicated short-range communication (DSRC), and third/fourth generation cellular networks (3G/4G). In this network area, vehicle users can use wireless fidelity access points (Wi-Fi APs) to offload 4G long-term evolution (4G-LTE) networks. However, when using Wi-Fi APs, the vehicles must organize themselves and select an appropriate mobile gateway (MGW) to communicate to the cellular infrastructure. Researchers are facing the problem of selecting the best MGW vehicle to aggregate vehicle traffic and reduce LTE load in HetVNETs when the Wi-Fi APs are unavailable for offloading. The selection process utilizes extra network overhead and complexity due to the frequent formation of clusters in this highly dynamic environment. In this study, we proposed a non-cluster adaptive QoS-aware gateway selection (AQAGS) scheme that autonomously picks a limited number of vehicles to act as LTE gateways based on the LTE network’s load status and vehicular ad hoc network (VANET) application’s QoS requirements. The present AQAGS scheme focuses on highway scenarios. The proposed scheme was evaluated using simulation of Urban mobility (SUMO) and network simulator version 2 (NS2) simulators and benchmarked with the clustered and non-clustered schemes. A comparison was made based on the end-to-end delay, throughput, control packet overhead (CPO), and packet delivery ratio (PDR) performance metrics over Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP) applications. Using VoIP, the AQAGS scheme achieved a 26.7% higher PDR compared with the other schemes.

Adaptive Points Sampling for Implicit Field Reconstruction of Industrial Digital Twin.

Nowadays, the digital twin (DT) plays an important role in Industry 4.0. It aims to model reality in the digital space for further industrial maintenance, management, and optimization. Previously, many AI technologies have been applied in this field and provide strong tools to connect physical and virtual spaces. However, we found that single-view 3D reconstruction (SVR) for DT has not been thoroughly studied. SVR can generate 3D digital models of real industrial products from just a single image. The application of SVR technology would bring convenience, cheapness, and robustness to modeling physical objects in digital space. However, the existing SVR methods cannot perform well in the reconstruction of details, which is indispensable and challenging in industrial products. In this paper, we propose a new detail-aware feature extraction network based on a feature pyramid network (FPN) for better detail reconstruction. Then, an extra network is designed to combine convolutional feature maps from different levels. Moreover, we also propose a novel adaptive points-sampling strategy to adaptively change the learning difficulty according to the training status. This can accelerate the training process and improve the fine-tuned network performance as well. Finally, we conduct comprehensive experiments on both the general objects dataset ShapeNet and a collected industrial dataset to prove the effectiveness of our methods and the practicability of the SVR technology for DT.

Privacy invasion via smart-home hub in personal area networks

Smart-home devices are being increasingly used in our daily lives. While these devices provide convenient functions to users, such convenience may come at a greater cost, such as the leakage of the user’s private information. This paper presents a system ChatterHub to address privacy risks in smart-home devices. Specifically, this work focuses on the devices that use Zigbee or Z-wave and are controlled by a centralized smart-home hub in a personal area network (PAN) for connecting to the Internet. ChatterHub passively eavesdrops on encrypted network traffic from the hub and leverages machine learning techniques to classify events and states of smart-home devices. We deployed ChatterHub on three real-world smart-home settings to evaluate its accuracy and efficiency. The evaluation results show that the attacker can successfully disclose smart-home devices’ behaviors with over 89% of recall and F1-score. We also demonstrate that an attacker can interfere with the smart-home hub’s communication and selectively drop packets to disable alerting users of a device’s status, such as security sensors and smart-locks. Furthermore, as a mitigation approach, we developed a packet-injection approach to effectively prevent threats from ChatterHub by generating only 9.2 MB of extra network traffic per day.

Doubling the ADMD in housing schemes to cater for future electrification of heat and transport

The rate at which electrification of transport (and heat) will take place, and what the exact impact on the network will actually be is uncertain, yet has to be catered for in any new network which is being designed. This is particularly the case in new Housing Schemes where there may be both an Electric Vehicle (EV) and a Heat Pump (HP) per house. Traditionally the ADMD estimate used was based on multiple small appliance loads acting in a statistically predictable manner, but with the addition of a 7kW EV, or a 3.5kW HP, both of which operate for several hours, any historical ADMD predictions are now largely irrelevant. Accordingly correct design requires the accurate prediction of electrification development and the optimum network associated - which of course means that if the electrification growth is significantly different then either gross over-investment in the network required has taken place, or possibly even worse, inadequate investment has initially taken place so that the costs of subsequently adding extra network capacity in an existing development are now multiples of the original investment. Applying Real Options theory in a practical manner suggests that that at each stage in the development of a network there is scope to make choices to either expand, contract, delay, abandon or redeploy assets. This suggests that designing the network to be able to exercise such flexibility could provide much more robust solutions in the face of uncertainty. Accordingly, when examined in this manner, it was found that doubling the existing ADMD from 2.5kVA to. 5.5kVA per customer provided the largest increase in ADMD at least cost to cover future unknown expansions in electrification loads. Furthermore, such doubling only adds about 10% to the overall costs of the original network development previously required, as the marginal cost of a slightly larger transformer is low as a percentage of the overall cost, and provision of extra cable outlets to offload such transformers (if even increased further in future) is even less. On the other hand, designing for a lower ADMD would save very little, but mean that the cost, if future expansion were required, would be very high. Development of modified Substation designs to facilitate uprating from 630kVA to 1000kVA allows scope for even further load increases if required, yet their initial deferral avoids stranding by future network development e.g. DSM.

Adversarial multi-task learning with inverse mapping for speech enhancement

Adversarial Multi-Task Learning (AMTL) has demonstrated its promising capability of information capturing and representation learning, however, is hardly explored in speech enhancement. In this paper, we propose a novel adversarial multi-task learning with inverse mapping method for speech enhancement. Our method focuses on enhancing the generator’s capability of speech information capturing and representation learning. To implement this method, two extra networks (namely P and Q) are developed to establish the inverse mapping from the generated distribution to the input data domains. Correspondingly, two new loss functions (i.e., latent loss and equilibrium loss) are proposed for the inverse mapping learning and the enhancement model training with the original adversarial loss. Our method obtains the state-of-the-art performance in terms of speech quality (PESQ=2.93, CVOL=3.55). For speech intelligibility, our method can also obtain competitive performance (STOI=0.947). The experimental results demonstrate that our method can effectively improve speech representation learning and speech enhancement performance.

Gyral Hinges Account for the Highest Cost and the Highest Communication Capacity in a Corticocortical Network.

Prior studies reported the global structure of brain networks exhibits the "small-world" and "rich-world" attributes. However, the underlying structural and functional architecture highlighted by these graph theory findings hasn't been explicitly related to the morphology of the cortex. This could be attributed to the lower resolution of used folding patterns, such as gyro-sulcal patterns. By defining a novel gyral folding pattern, termed gyral hinge (GH), which is the conjunction of ordinary gyri from multiple directions, we found GHs possess the highest length and cost in the white matter fiber connective network, and the shortest paths in the network tend to travel through GHs in their middle part. Based on these findings, we would hypothesize GHs could reside in the centers of a network core, thereby accounting for the highest cost and the highest communication capacity in a corticocortical network. The following results further support our hypothesis: 1) GHs possess stronger functional network integration capacity. 2) Higher cost is found on the connection with GHs to hinges and GHs to GHs. 3) Moving GHs introduces higher extra network cost. Our findings and hypotheses could reveal a profound relationship among the cortical folding patterns, axonal wiring architectures, and brain functions.

BBW: a batch balance wrapper for training deep neural networks on extremely imbalanced datasets with few minority samples

In recent years, Deep Neural Networks (DNNs) have achieved excellent performance on many tasks, but it is very difficult to train good models from imbalanced datasets. Creating balanced batches either by majority data down-sampling or by minority data up-sampling can solve the problem in certain cases. However, it may lead to learning process instability and overfitting. In this paper, we propose the Batch Balance Wrapper (BBW), a novel framework which can adapt a general DNN to be well trained from extremely imbalanced datasets with few minority samples. In BBW, two extra network layers are added to the start of a DNN. The layers prevent overfitting of minority samples and improve the expressiveness of the sample distribution of minority samples. Furthermore, Batch Balance (BB), a class-based sampling algorithm, is proposed to make sure the samples in each batch are always balanced during the learning process. We test BBW on three well-known extremely imbalanced datasets with few minority samples. The maximum imbalance ratio reaches 1167:1 with only 16 positive samples. Compared with existing approaches, BBW achieves better classification performance. In addition, BBW-wrapped DNNs are 16.39 times faster, relative to unwrapped DNNs. Moreover, BBW does not require data preprocessing or additional hyper-parameter tuning, operations that may require additional processing time. The experiments prove that BBW can be applied to common applications of extremely imbalanced data with few minority samples, such as the classification of EEG signals, medical images and so on.

A New Physical Design Flow for a Selective State Retention Based Approach

This research presents a novel approach for physical design implementation aimed for a System on Chip (SoC) based on Selective State Retention techniques. Leakage current has become a dominant factor in Very Large Scale Integration (VLSI) design. Power Gating (PG) techniques were first developed to mitigate these leakage currents, but they result in longer SoC wake-up periods due to loss of state. The common State Retention Power Gating (SRPG) approach was developed to overcome the PG technique’s loss of state drawback. However, SRPG resulted in a costly expense of die area overhead due to the additional state retention logic required to keep the design state when power is gated. Moreover, the physical design implementation of SRPG presents additional wiring due to the extra power supply network and power-gating controls for the state retention logic. This results in increased implementation complexity for the physical design tools, and therefore increases runtime and limits the ability to handle large designs. Recently published works on Selective State Retention Power Gating (SSRPG) techniques allow reducing the total amount of retention logic and their leakage currents. Although the SSRPG approach mitigates the overhead area and power limitations of the conventional SRPG technique, still both SRPG and SSRPG approaches require a similar extra power grid network for the retention cells, and the effect of the selective approach on the complexity of the physical design has not been yet investigated. Therefore, this paper introduces further analysis of the physical design flow for the SSRPG design, which is required for optimal cell placement and power grid allocation. This significantly increases the potential routing area, which directly improves the convergence time of the Place and Route tools.

Assessment of Network &amp; Processor Virtualization in Cloud Computing

Cloud Computing is an emerging field in applied computer science. Cloud computing depends on virtualization, where a sole physical resource is virtualized into numerous virtual resources. Resources that are being virtualized include physical storage, processing units, volatile memories, network, and further physical resources. Processor and network virtualization offer several advantages like lessening hardware cost, energy consumption, and the human struggle for administration and management of resources. In this article, the primary concentration was on the evaluation and assessment of network virtualization between different cloud computing hypervisors and to contrast their evaluated results. This comparison is based on a few experiments performed on Citrix XenServer, Microsoft Hyper-V, and VMware vSphere hypervisors. XenServer uses Open vSwitch for its network virtualization whereas Hyper-V and vSphere use Microsoft network virtualization and vSphere switch for managing their network virtualization respectively. We have found that virtual networks are further scalable and secure than physical networks. We can assign multiple virtual interfaces or connections to a single virtual machine. In light of various experiments, in which we have compared non-virtualized with virtualized scenarios, for all these above-mentioned hypervisors, we found out that we can use virtual networks with and without VLAN Tags or VLAN Ids. Hyper-V provides further flexibility and options regarding VLAN Tag in comparison with XenServer or vSphere. We have also found that XenServer and vSphere give us extra network throughput in comparison with Hyper-V in terms of TCP and UDP whereas Round Trip Time (RTT) in the virtual network of all hypervisors are high in comparison with the non-virtual environment. Network CPU usage is also measured and found that vSphere and XenServer together consume more CPU during network activity in comparison with Hyper-V. We also found that processor virtualization did not majorly result from the average schedule time in comparison with a non-virtualized machine. We consequently conclude that in general a small reduction in the performance, in the case of virtualization is insignificant as compared with the advantages we get from virtualization when using cloud hypervisors. For the application of virtualization, this work will motivate to set up of data centers using a virtualized environment.