RWRoute: An Open-source Timing-driven Router for Commercial FPGAs

One of the key obstacles to pervasive deployment of FPGA accelerators in data centers is their cumbersome programming model. Open source tooling is suggested as a way to develop alternative EDA tools to remedy this issue. Open source FPGA CAD tools have traditionally targeted academic hypothetical architectures, making them impractical for commercial devices. Recently, there have been efforts to develop open source back-end tools targeting commercial devices. These tools claim to follow an alternate data-driven approach that allows them to be more adaptable to the domain requirements such as faster compile time. In this paper, we present RWRoute, the first open source timing-driven router for UltraScale+ devices. RWRoute is built on the RapidWright framework and includes the essential and pragmatic features found in commercial FPGA routers that are often missing from open source tools. Another valuable contribution of this work is an open-source lightweight timing model with high fidelity timing approximations. By leveraging a combination of architectural knowledge, repeating patterns, and extensive analysis of Vivado timing reports, we obtain a slightly pessimistic, lumped delay model within 2% average accuracy of Vivado for UltraScale+ devices. Compared to Vivado, RWRoute results in a 4.9× compile time improvement at the expense of 10% Quality of Results (QoR) loss for 665 synthetic and six real designs. A main benefit of our router is enabling fast partial routing at the back-end of a domain-specific flow. Our initial results indicate that more than 9× compile time improvement is achievable for partial routing. The results of this paper show how such a router can be beneficial for a low touch flow to reduce dependency on commercial tools.

Data Dissemination for Industry 4.0 Applications in Internet of Vehicles Based on Short-term Traffic Prediction

As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.

5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0

Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among PLs in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the E2E mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of TSN against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.

Effects of Drugs Reabsorption Time Delays for a Pharmacokinetics Model in Humans

A three-compartmental delay model is formulated to describe the pharmacokinetics of drugs subjected to both intravenous and oral doses with reabsorptions by the central compartment. Model dynamics are analyzed rigorously, and two equilibrium points are obtained to be locally asymptotically stable under certain conditions. Time delays used as lags in reabsorption of drugs by central compartment from other two compartments caused rebounds or peaks and fluctuations in the time profiles for amounts of drug in all the compartments. Sensitivity analysis revealed that elimination rates decrease the amounts in all compartments. Furthermore, reabsorption rates cause superimposition at the initial phases of the drug amount profiles; subsequently, the quantities decrease in compartment one and increase in compartments two and three, respectively.

Estimation of Traffic Delay Due to U-Turns at Uncontrolled Medians: Case Study in Phnom Penh, Cambodia

Lack of infrastructure capacity to the demand in mobility results in delays in traffic. U-turns restrict the movement of the opposing through-traffic during the maneuver, hence triggering delays. Significant delays can be observed, especially in high volume traffic, where U-turning is not restricted. Hence, it is imperative to study the delays incurred by the U-turning vehicles to assess their impacts on traffic congestion. This study thus proposes a quantitative method to estimate the aggregate and average delay per vehicle caused by U-turns at uncontrolled medians of urban roads. Traffic data were collected at a corridor where vehicles were allowed to U-turn on a four-lane divided mixed traffic urban road in Phnom Penh, Cambodia. The delay in the opposing lane of traffic was calculated by analyzing the data using a novel analytical method; a derivative of the Webster’s delay model. The results reveal that the suggested technique evaluates the caused delay with good accuracy. Excessive traffic delay and about three seconds delay per vehicle in the through-traffic of the opposite lane align well with the experienced delay at the site. The method can be used to assess the delay at straight corridor sections where U-turns are not restricted, understand the causes of the delay, and develop control measures for improved traffic performance.

Resource Prediction-Based Edge Collaboration Scheme for Improving QoE

Recent years have witnessed a growth in the Internet of Things (IoT) applications and devices; however, these devices are unable to meet the increased computational resource needs of the applications they host. Edge servers can provide sufficient computing resources. However, when the number of connected devices is large, the task processing efficiency decreases due to limited computing resources. Therefore, an edge collaboration scheme that utilizes other computing nodes to increase the efficiency of task processing and improve the quality of experience (QoE) was proposed. However, existing edge server collaboration schemes have low QoE because they do not consider other edge servers’ computing resources or communication time. In this paper, we propose a resource prediction-based edge collaboration scheme for improving QoE. We estimate computing resource usage based on the tasks received from the devices. According to the predicted computing resources, the edge server probabilistically collaborates with other edge servers. The proposed scheme is based on the delay model, and uses the greedy algorithm. It allocates computing resources to the task considering the computation and buffering time. Experimental results show that the proposed scheme achieves a high QoE compared with existing schemes because of the high success rate and low completion time.

A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells

We propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse spatio-temporal gene expression in intact networks of cells. The model describes the oscillating time evolution of molecular mRNA counts through a negative transcriptional-translational feedback loop encoded in a chemical Langevin equation with a probabilistic delay distribution. The model is extended spatially by means of a multiplicative random effects model with a first order Markov random field prior distribution. Our methodology effectively separates intrinsic molecular noise, measurement noise, and extrinsic noise and phenotypic variation driving cell heterogeneity, while being amenable to parameter identification and inference. Based on the single-cell model we propose a novel computational stability analysis that allows us to infer two key characteristics, namely the robustness of the oscillations, i.e. whether the reaction network exhibits sustained or damped oscillations, and the profile of the regulation, i.e. whether the inhibition occurs over time in a more distributed versus a more direct manner, which affects the cells’ ability to phase-shift to new schedules. We show how insight into the spatio-temporal characteristics of the circadian feedback loop in the suprachiasmatic nucleus (SCN) can be gained by applying the methodology to bioluminescence-reported expression of the circadian core clock gene Cry1 across mouse SCN tissue. We find that while (almost) all SCN neurons exhibit robust cell-autonomous oscillations, the parameters that are associated with the regulatory transcription profile give rise to a spatial division of the tissue between the central region whose oscillations are resilient to perturbation in the sense that they maintain a high degree of synchronicity, and the dorsal region which appears to phase shift in a more diversified way as a response to large perturbations and thus could be more amenable to entrainment.

A generalized distributed delay model for HBV infection with two modes of transmission and adaptive immunity: A mathematical study

In this paper, we formulate a generalized hepatitis B virus (HBV) infection model with two modes of infection transmission and adaptive immunity, and investigate its dynamical properties. Both the virus-to-cell and cell-to-cell infection transmissions are modeled by general functions which satisfy some biologically motivated assumptions. Furthermore, the model incorporates three distributed time delays for the production of active infected hepatocytes, mature capsids and virions. The well-posedness of the proposed model is established by showing the non-negativity and boundedness of solu- tions. Five equilibria of the model are identified in terms of five threshold parameters R0, R1, R2, R3 and R4. Further, the global stability analysis of each equilibrium under certain conditions is carried out by employing suitable Lyapunov function and LaSalle’s invariance principle. Finally, we present an example with numerical simulations to il- lustrate the applicability of our study. Nonetheless, the results obtained in this study are valid for a wide class of HBV infection models.