Routing Congestion Research Articles

Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data

Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.

Predicting routability of FPGA design by learning complex network images

Accurately and efficiently predicting the routability of modern FPGAs in the early stages is significant for achieving ultimate optimization. We propose a novel approach for FPGA routability prediction by the CBAM-CNN model incorporating an attention mechanism to learn complex network images. For an FPGA placement design, we innovatively extract the circuit-related and complex network-related features associated with routing congestion and map them into a complex network image. The approach demonstrates a remarkable performance in routability prediction, surpassing the recent machine learning models in accuracy, precision, sensitivity, and specificity. Notably, the Matthews correlation coefficient has improved by 6.15% compared with the state-of-the-art convolutional neural network model (Alhyari et al., 2019). The additional ROC (Receiver Operating Characteristic Curve) analysis highlights the model’s classification efficiency. We estimate the significance of complex network features in predicting FPGA design routability, presenting in descending order of Degree, Strength, Eigenvector, and Betweenness.

Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics

We have proposed two algorithms in this paper for dynamic traffic in elastic optical networks (EON). Algorithm1 considers the spatial access blocking probability metric (SABPM) during dynamic operation and route traffic to a path with lower SABPM. Therefore, it considers a path with more contiguous blocks to support heterogeneous bandwidths (BW) and modulation schemes. Algorithm1 keeps a balance of fragmentation on available routes. Algorithm2 makes routing decision for dynamic traffic based on the maximum idle slots as well as minimum SABPM. Therefore, it selects a less fragmented path with more number of available contiguous blocks to support heterogeneous BW. Algorithm2 balances the path utilization and avoids over utilization of congested route. We have evaluated the performance of both algorithms in terms of spatial external fragmentation, contiguity ratio, contiguous aligned spectrum ratio, SABPM, and BW blocking probability (BwBP). Python with seaborn, pandas, and other libraries are used for network analytics. Results show that resource utilization improves with the proposed algorithms. It has been shown that algorithm2 and algorithm1 achieve 27% and 18% traffic gains respectively over the alternate routing.

PASTA: Programming and Automation Support for Scalable Task-Parallel HLS Programs on Modern Multi-Die FPGAs

In recent years, the adoption of FPGAs in datacenters has increased, with a growing number of users choosing High-Level Synthesis (HLS) as their preferred programming method. While HLS simplifies FPGA programming, one notable challenge arises when scaling up designs for modern datacenter FPGAs that comprise multiple dies. The extra delays introduced due to die crossings and routing congestion can significantly degrade the frequency of large designs on these FPGA boards. Due to the gap between HLS design and physical design, it is challenging for HLS programmers to analyze and identify the root causes, and fix their HLS design to achieve better timing closure. Recent efforts have aimed to address these issues by employing coarse-grained floorplanning and pipelining strategies on task-parallel HLS designs where multiple tasks run concurrently and communicate through FIFO stream channels. However, many applications are not streaming friendly and many existing accelerator designs heavily rely on buffer channel based communication between tasks. In this work, we take a step further to support a task-parallel programming model where tasks can communicate via both FIFO stream channels and buffer channels. To achieve this goal, we design and implement the PASTA framework, which takes a large task-parallel HLS design as input and automatically generates a high-frequency FPGA accelerator via HLS and physical design co-optimization. Our framework introduces a latency-insensitive buffer channel design, which supports memory partitioning and ping-pong buffering while remaining compatible with vendor HLS tools. On the frontend, we provide an easy-to-use programming model for utilizing the proposed buffer channel; while on the backend, we implement efficient placement and pipelining strategies for the proposed buffer channel. To validate the effectiveness of our framework, we test it on four widely used Rodinia HLS benchmarks and two real-world accelerator designs and show an average frequency improvement of 25%, with peak improvements of up to 89% on AMD/Xilinx Alveo U280 boards compared to Vitis HLS baselines.

Design of urban low-carbon green transportation demonstration area in the context of "dual carbon" --Taking Wuhua District of Kunming City (Zhengyi Road and Sanshi Street area) as an example

In the context of "dual-carbon", by reconstructing the relationship between people and vehicles, optimizing spatial quality, and taking street space renovation as a starting point, we can continuously improve the travel experience of human-centered neighborhoods, build a higher-quality living environment, and guide green travel. Under this context, this study takes Wuhua District, Kunming, Zhengyi Road and Sanshi Street as a case to explore the design of urban low-carbon green transportation demonstration area under the background of "dual-carbon". By analyzing the "dual-carbon" strategy and related policies, and taking into account the current transportation situation in Wuhua District, Kunming, it is found that there are problems such as traffic congestion, chaotic parking, and poor bus routes in this area. To address these challenges, this study proposes a series of optimization measures based on successful cases at home and abroad, including improving the transportation network, strengthening non-motorized parking management, introducing intelligent traffic management systems, and optimizing the connection between bus routes and rail transit. These measures will help improve transportation efficiency, reduce congestion, promote green travel, and provide useful lessons and references for the construction of low-carbon transportation demonstration zones in other cities.

A new priority aware routing protocol for efficient emergency data transmissions in MANETs

This paper introduces a new priority-aware routing protocol for mobile Ad-hoc networks to be utilized in emergencies, which is based on AODV. Mobile Ad-hoc networks find extensive use in various domains including military operations, environmental monitoring, healthcare, disaster response, smart transportation systems, unmanned aerial vehicles, and smart homes. During emergencies, communication can be severely restricted or even impossible due to the congestion of physical communication channels and unexpected technical failures in the infrastructure. Mobile Ad-hoc networks offer a solution to maintain continuous and reliable communication under such challenging conditions. In emergency scenarios, it is crucial for any node in the network to promptly deliver urgent messages to the intended destination, especially when certain nodes require ongoing active communication. The proposed routing protocol effectively addresses this requirement through its priority-aware mechanisms. The protocol ensures that nodes not involved in emergency tasks select the least congested route to prevent any delays or disruptions in the transmission of critical emergency data. This approach guarantees seamless communication for emergency nodes while allowing non-emergency nodes to communicate with each other as well. The study proposed in this paper introduces a new priority-aware routing protocol based on AODV for mobile Ad-hoc networks in emergencies. The packet transmission ratio of emergency nodes within the network is improved while maintaining the overall network performance unaffected. The adoption of proposed mechanisms to enhance performance does not necessitate an expansion in the size of data and control packets. These mechanisms do not inflict any supplementary latency or incur packet loss expenses on the network. The proposed protocol has been implemented and evaluated using ns-3 simulation software across various emergency scenarios. The results show that emergency nodes using the proposed protocol, achieve better packet delivery ratios compared to the original AODV, DSR, P-AODV, and AOMDV protocols, with improvements of 10.8%, 15.9%, 6.2%, and 5.9% respectively. This improvement in the packet delivery ratio for emergency data traffic is achieved without causing any disruptions in the overall network communication flow.

New High-Rate Timestamp Management with Real-Time Configurable Virtual Delay and Dead Time for FPGA-Based Time-to-Digital Converters

Modern applications require the ability to measure time events with high resolution, a full-scale range, and multiple input channels. Time-to-Digital Converters (TDCs) are a popular option to convert time intervals into timestamps. To reduce the time-to-market and Non-Recurring Engineering (NRE) costs, a Field-Programmable Gate Array (FPGA) implementation has been chosen. The high number of requested bits and channels, however, gives rise to routing congestion issues when routed in a parallel manner. In this paper, we will propose and analyze a novel solution, the Belt-Bus (BB), which involves a parallel-to-serial conversion of the timestamp stream coming from the TDC while maintaining chronological order and a sufficient high rate, and flagging the presence of timestamp overflow. Moreover, two new useful features are added. The first is a “Virtual Delay” to compensate for offsets due to cable length and FPGA routing path mismatch. The second is a “Virtual Dead-Time” to filter out unforeseen events. Finally, the BB was tested on a Xilinx 28 nm 7-Series Kintex-7 325T FPGA, achieving an overall data rate of 199.9 Msps with very limited resource usage (i.e., lower than a total of 4.5%), consuming only 480 mW in a 16-channel implementation.

End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism.

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.

An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips

The deadlock-free and live lock-free routing at the same time is minimized in the network on chip (NoC) using the proposed adoptive reconfigurable routing protocol (ARRP). Congestion condition emergencies are avoided using the proposed algorithm. The input packet distribution process is improved among all its shortest paths of output points. The performance analysis has been initiated by considering different configuration (N*N) mesh networks, by sending various ranges of data packets to the network on chip. The average and maximum power dissipation of XY, odd-even, Dy-XY algorithm, and proposed algorithm are determined. In this paper, an analysis of gate utilization during data packet transfer in various mesh configurations is carried out. The number of cycles required for each message injection in different mesh configurations is analyzed. The proposed routing algorithm is implemented and compared with conventional algorithms. The simulation has been carried out using reconfigurable two-dimensional mesh for the NoC. The proposed algorithm has been implemented considering array size, the routing operating frequency, link width length, value of probability, and traffic types. The proposed ARRP algorithm reduces the average latency, avoids routing congestion, and is more feasible for NoC compared to conventional methods.

A four-state Markov model for modelling bursty traffic and benchmarking of random early detection

Active Queue Management (AQM) techniques are crucial for managing packet transmission efficiently, maintaining network performance, and preventing congestion in routers. However, achieving these objectives demands precise traffic modeling and simulations in extreme and unstable conditions. The internet traffic has distinct characteristics, such as aggregation, burstiness, and correlation. This paper presents an innovative approach for modeling internet traffic, addressing the limitations of conventional modeling and conventional AQM methods' development, which are primarily designed to stabilize the network traffic. The proposed model leverages the power of multiple Markov Modulated Bernoulli Processes (MMBPs) to tackle the challenges of traffic modeling and AQM development. Multiple states with varying probabilities are used to model packet arrivals, thus capturing the burstiness inherent in internet traffic. Yet, the overall probability is maintained identical, irrespective of the number of states (one, two, or four), by solving linear equations with multiple variables. Random Early Detection (RED) was used as a case study method with different packet arrival probabilities based on MMBPs with one, two, and four states. The results showed that the proposed model influences the outcomes of AQM methods. Furthermore, it was found that RED might not effectively address network burstiness due to its relatively slow reaction time. As a result, it can be concluded that RED performs optimally only with a single-state model.

Analyzing, Identifying & Alerting on Network Issues

Analyzing, Identifying & Alerting on Network Issues

Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction for Designs in Advanced Technology Nodes With Consolidated Practical Applicability and Sustainability

Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction for Designs in Advanced Technology Nodes With Consolidated Practical Applicability and Sustainability

ПРОЕКТУВАННЯ БАЗИ ДАНИХ ПОЇЗДОК КОРИСТУВАЧІВ ГРОМАДСЬКОГО ТРАНСПОРТУ З ЕЛЕМЕНТАМИ ТЕХНОЛОГІЇ DATA WAREHOUSE

The use of databases to record travel in city transport is a widespread and effective practice. Databases allow you to organize the storage, management and processing of information about passenger journeys, timetables, tariffs and other related data.The main methods of using databases to record journeys in urban transport include, for example, storing information about journeys when the database can contain information about each journey of a passenger, including time, date, place of boarding and disembarking, route number and other details. This allows you to track and analyze travel data to improve planning and optimization of transport services. The next example of using databases is managing timetables and routes, since the database can contain information about public transport timetables, routes and stops. This allows you to automate the processes of planning and updating timetables, as well as providing passengers with up-to-date information about available routes and the time of arrival of transport. Databases in transport systems can also be used to record fares, that is, to record passenger payments and manage various tariff zones or payment systems, such as electronic tickets or contactless cards. Information about passenger payments can be recorded and processed in the database for subsequent analysis and reporting, which allows analysis of data on travel, payment, passenger traffic and other parameters to identify trends, resource planning and management decisions. You can create reports and dashboards that provide information about route congestion, the popularity of certain stops, and other useful metrics. Ultimately, the database can be integrated with other traffic management systems, such as traffic flow monitoring systems, traffic light control systems, or passenger information systems. This allows you to create a complex solution that optimizes traffic and improves the experience of passengers.The use of databases to record travel in city transport contributes to a more efficient organization and management of transport services, and also allows analyzing data to improve the public transport system as a whole.The purpose of this research is to formulate the requirements and determine the shape of the data base of the fare payment system in urban transport, which would take into account the fares of all categories of citizens and would allow receiving complete and timely information for managing traffic flows, tariffs and effective spending of budget funds directed to compensation for the fares of privileged categories of citizens.

OIL SPILL SIMULATION IN THE BALI STRAIT USING THE GNOME AND FVCOM MODELS ON EASTERLY SEASON

Among several forms of marine pollution, oil damages coastal ecosystems. Repeated reports of oil contamination in the marine environment can partly load with major shipping lines. The Ketapang-Gilimanuk crossing over the Bali Strait is Indonesia's second busiest ferry port after Merak-Bakauheni. The most congested shipping routes can carry a significant risk of environmental damage from an oil spill. Oil spill trajectory modelling is carried out to reduce the impact of this possible disaster. Therefore, the use of modelling to ascertain the route of the oil spill was considered. The oil leak path is simulated using the GNOME model. Two oil spill scenarios were used as input models. The chosen location is around the Ketapang Gilimanuk crossing, as well as the time of the easterly season. An ocean HF radar stationed in the Bali Strait verifies the accuracy of the current marine data generated by FVCOM. To see the pattern of the oil spill trajectory based on a ten days simulation, we combined the latest data from FVCOM with GNOME. To determine the ability of the model to predict the flow and trajectory of oil spills in the Bali Strait, this study try to analyze and interpret the oil spill trajectory from model and then validate the model results with satellite imagery.
 Keywords: Bali strait; oil spill; trajectory; GNOME; FVCOM; HF Radar

Linguistic interval type 2 fuzzy logic-based Exigency Vehicle routing: IoT system development for smart city applications with soft computing-based optimization

An Exigency Vehicle (EV) has to go more quickly to increase the likelihood that someone in danger would survive. Construction projects, strikes, and accidents may all be avoided with an effective vehicle routing solution. The Internet of Things (IoT) network simulation for Exigency Vehicle routing is proposed in this research utilizing a linguistic interval type 2 fuzzy logic system (LIT2FLS) based data fusion approach. The predicted effectiveness of our model for intelligent city applications, including emergency vehicle routing, is well demonstrated by the LIT2FLS correlation coefficients of 0.994%. This data fusion approach determines the exact level of congestion for a given place by combining sensory data with crowd reactions. In addition, OSRM employs a road communication system to detect real-time traffic variations and choose the least congested route. Furthermore, a sensor station for gathering the speeds and pollutants of moving automobiles on the road has also been established. An Android app has been developed to collect public information on blockages A driver of an Exigency Vehicle (EV) is directed towards a medical facility with the quickest, congestion-aware path by means of the Application software service. We have created an IT2FLS system that assists with decisions to calculate traffic congestion. Analyze the ability to scale and quickness of response associated with the suggested routing strategy.

CHIP-KNNv2: AConfigurable andHigh-PerformanceK-NearestNeighbors Accelerator on HBM-based FPGAs

The k-nearest neighbors (KNN) algorithm is an essential algorithm in many applications, such as similarity search, image classification, and database query. With the rapid growth in the dataset size and the feature dimension of each data point, processing KNN becomes more compute and memory hungry. Most prior studies focus on accelerating the computation of KNN using the abundant parallel resource on FPGAs. However, they often overlook the memory access optimizations on FPGA platforms and only achieve a marginal speedup over a multi-thread CPU implementation for large datasets.In this article, we design and implement CHIP-KNN: an HLS-based, configurable, and high-performance KNN accelerator. CHIP-KNN optimizes the off-chip memory access on modern HBM-based FPGAs such as the AMD/Xilinx Alveo U280 FPGA board. CHIP-KNN is configurable for all essential parameters used in the algorithm, including the size of the search dataset, the feature dimension and data type representation of each data point, the distance metric, and the number of nearest neighbors - K. In terms of design architecture, we explore and discuss the tradeoffs between two design versions: CHIP-KNNv1 (Ping-Pong buffer based) and CHIP-KNNv2 (streaming-based). Moreover, we investigate the routing congestion issue in our accelerator design, implement hierarchical structures to shorten critical paths, and integrate an open-source floorplanning optimization tool called TAPA/AutoBridge to eliminate the place-and-route issues. To explore the design space and balance the computation and memory access performance, we also build an analytical performance model. Given a user configuration of the KNN parameters, our tool can automatically generate TAPA HLS C code for the optimal accelerator design and the corresponding host code, on the HBM-based FPGA platform.Our experimental results on the Alveo U280 show that, compared to a 48-thread CPU implementation, CHIP-KNNv2 achieves a geomean performance speedup of 15×, with a maximum speedup of 45×. Additionally, we show that CHIP-KNNv2 achieves up to 2.1× performance speedup over CHIP-KNNv1 while increasing configurability. Compared with the state-of-the-art Facebook AI Similarity Search (FAISS) [23] GPU implementation running on a Nvidia Tesla V100 GPU, CHIP-KNNv2 achieves an average latency reduction of 30.6× while requiring 34.3% of GPU power consumption.

Smartphone mobility assistants. A lever to guide route choice preferences in mass transit?

The regulation of passenger congestion in mass transit is a persistent issue that requires ingenious and cost-effective solutions to ensure that related operations run at optimum capacity. Mass transit operators may implement Public Transport Demand Management (PTDM) strategies like cognitive levers to tackle this issue by targeting passengers’ behaviour during the route planning that precedes travel in mass transit. In this regard, multiple experimental studies in cognitive psychology and sciences have shown that transit maps can be used to guide passengers’ route choice preferences in mass transit.A route choice experiment was conducted to examine the potential of smartphone mobility assistants as a tool to guide mass transit users’ away from the fastest options that tend to be predominantly preferred. 582 participants took part in an online study where they engaged in a route selection task by indicating their route choices in the Île-de-France mass transit system. We measured how participants’ preferences for the fastest route varied depending on the visual format in which routes were presented (on a transit map, on a timeline or listed briefly as per current trends), the presence of conflicting visuo-spatial information on the transit map (fastest choice = shortest vs. longest choice) and the level of comfort (availability of simple transfers and/or less congested routes).The main results suggest the existence of two levers than can be implemented in smartphone mobility assistants to manage passenger congestion in mass transit: (1) a perceptive heuristic whereby passengers presented with a transit map manifest a preference for the route presented as the shortest on the map and (2) the possibility to sway a proportion of passengers away from the choice of the fastest route by presenting information about the comfort levels of alternative options.

WIRELESS TRAFFIC AND ROUTING ENHANCEMENT USING EMPEROR PENGUIN OPTIMIZER GUIDED BY CONDITIONAL GENERATIVE ADVERSARIAL NETS

The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.

An efficient reconfigurable geographic routing congestion control algorithm for wireless sensor networks

<span lang="EN-US">In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods</span>

An effective routability-driven packing algorithm for large-scale heterogeneous FPGAs

With the growing complexity and scale of FPGA architectures, challenges posed by heterogeneity and routability have become more pronounced in FPGA packing. Conventional FPGA packing algorithms are seldom considered routability and may lead to unroutable results. This paper addresses these challenges by proposing an effective routability-driven packing algorithm tailored for large-scale heterogeneous FPGAs. The proposed algorithm consists of four major parts: (1) a LUT-FF pairing method is presented to pack LUTs and FFs into LUT-FF pairs. (2) A novel BLE packing method is presented to deal with the complex clock constraints, meanwhile forming more HCLB-friendly BLEs. (3) A congestion-aware HCLB packing technique is proposed to produce placement-friendly netlists without degrading routability. (4) An effective and accurate routing congestion estimation method is proposed to guide the packing process. Based on the ISPD 2017 clock-aware FPGA placement contest benchmarks, experimental results show that our algorithm outperforms three state-of-the-art FPGA placers by 9.8%, 8.2%, and 3.4% on routed wirelength, respectively.