Efficient approaches in interconnect-driven floorplanning.

Lai Tsz Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 123-129).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- VLSI Design Cycle --- p.2Chapter 1.2 --- Physical Design Cycle --- p.4Chapter 1.3 --- Floorplanning --- p.7Chapter 1.3.1 --- Types of Floorplan and Floorplan Representations --- p.11Chapter 1.3.2 --- Interconnect-driven Floorplanning --- p.13Chapter 1.4 --- Motivations and Contributions --- p.17Chapter 1.5 --- Organization of this Thesis --- p.18Chapter 2 --- Literature Review on Floorplan Representation --- p.20Chapter 2.1 --- Slicing Floorplan Representation --- p.20Chapter 2.1.1 --- Normalized Polish Expression --- p.20Chapter 2.2 --- Non-slicing Floorplan Representations --- p.21Chapter 2.2.1 --- Sequence Pair (SP) --- p.21Chapter 2.2.2 --- Bounded-sliceline Grid (BSG) --- p.23Chapter 2.2.3 --- O-tree --- p.25Chapter 2.2.4 --- B*-tree --- p.26Chapter 2.3 --- Mosaic Floorplan Representations --- p.28Chapter 2.3.1 --- Corner Block List (CBL) --- p.28Chapter 2.3.2 --- Twin Binary Trees (TBT) --- p.31Chapter 2.3.3 --- Twin Binary Sequences (TBS) --- p.32Chapter 2.4 --- Summary --- p.34Chapter 3 --- Literature Review on Interconnect Optimization in Floorplan- ning --- p.37Chapter 3.1 --- Wirelength Estimation --- p.37Chapter 3.2 --- Congestion Optimization --- p.38Chapter 3.2.1 --- Integrated Floorplanning and Interconnect Planning --- p.41Chapter 3.2.2 --- Multi-layer Global Wiring Planning (GWP) --- p.43Chapter 3.2.3 --- Estimating Routing Congestion using Probabilistic Anal- ysis --- p.44Chapter 3.2.4 --- Congestion Minimization During Placement --- p.46Chapter 3.2.5 --- Modelling and Minimization of Routing Congestion --- p.48Chapter 3.3 --- Buffer Planning --- p.49Chapter 3.3.1 --- Buffer Clustering with Feasible Region --- p.51Chapter 3.3.2 --- Routability-driven Repeater Clustering Algorithm with Iterative Deletion --- p.55Chapter 3.3.3 --- Planning Buffer Locations by Network Flow --- p.58Chapter 3.3.4 --- Buffer Planning using Integer Multicommodity Flow --- p.60Chapter 3.3.5 --- Buffer Planning Problem using Tile Graph --- p.60Chapter 3.3.6 --- Probabilistic Analysis for Buffer Block Planning --- p.62Chapter 3.3.7 --- Fast Buffer Planning and Congestion Optimization --- p.63Chapter 3.4 --- Summary --- p.66Chapter 4 --- Congestion Evaluation: Wire Density Model --- p.68Chapter 4.1 --- Introduction --- p.68Chapter 4.2 --- Overview of Our Floorplanner --- p.70Chapter 4.3 --- Wire Density Model --- p.71Chapter 4.3.1 --- Computation of Ni --- p.72Chapter 4.3.2 --- Computation of Pi --- p.74Chapter 4.3.3 --- Usage of Mirror TBT --- p.76Chapter 4.4 --- Implementation --- p.76Chapter 4.4.1 --- Efficient Calculation of Ni --- p.76Chapter 4.4.2 --- Solving the LCA Problem Efficiently --- p.81Chapter 4.4.3 --- Cost Function --- p.81Chapter 4.4.4 --- Complexity --- p.81Chapter 4.5 --- Experimental Results --- p.82Chapter 4.6 --- Conclusion --- p.83Chapter 5 --- Buffer Planning: Simple Buffer Planning Method --- p.85Chapter 5.1 --- Introduction --- p.85Chapter 5.2 --- Variable Interval Buffer Insertion Constraint --- p.87Chapter 5.3 --- Overview of Our Floorplanner --- p.88Chapter 5.4 --- Buffer Planning --- p.89Chapter 5.4.1 --- Feasible Grids --- p.89Chapter 5.4.2 --- Table Look-up Approach --- p.89Chapter 5.5 --- Implementation --- p.91Chapter 5.5.1 --- Building the Look-up Tables --- p.91Chapter 5.5.2 --- An Example of Look-up Table Construction --- p.94Chapter 5.5.3 --- A Faster Approach for Building the Look-up Tables --- p.101Chapter 5.5.4 --- An Example of the Faster Look-up Table Construction --- p.105Chapter 5.5.5 --- I/O Pin Locations --- p.106Chapter 5.5.6 --- Cost Function --- p.110Chapter 5.5.7 --- Complexity --- p.111Chapter 5.6 --- Experimental Results --- p.112Chapter 5.6.1 --- Selected Value for A --- p.112Chapter 5.6.2 --- Performance of Our Floorplanner --- p.113Chapter 5.7 --- Conclusion --- p.116Chapter 6 --- Conclusion --- p.118Chapter A --- An Efficient Algorithm for the Least Common Ancestor Prob- lem --- p.120Bibliography --- p.12

Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag

Risk Minimizing Evacuation Strategies under Uncertainty

This paper presents results on the simulation of the evacuation of the city of Padang with approximately 1,000,000 inhabitants. The model used is MATSim (www.matsim.org). Three different strategies were applied: shortest path solution, user optimum, system optimum, together with a constraint that moves should reduce risk whenever possible. The introduction of the risk minimization increases the overall required safe egress time (RSET). The differences between the RSET for the three risk minimizing strategies are small. Further quantities used for the assessment of the evacuation are the formation of congestion and the individual RSETs (in comparison with the available SET).BMBF, 03G0666E, Verbundprojekt FW: Last-mile Evacuation; Vorhaben: Evakuierungsanalyse und Verkehrsoptimierung, Evakuierungsplan einer Stadt - Sonderprogramm GEOTECHNOLOGIENBMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacitie

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Throughput-driven floorplanning with wire pipelining

The size of future high-performance SoC is such that the time-of-flight of wires connecting distant pins in the layout can be much higher than the clock period. In order to keep the frequency as high as possible, the wires may be pipelined. However, the insertion of flip-flops may alter the throughput of the system due to the presence of loops in the logic netlist. In this paper, we address the problem of floorplanning a large design where long interconnects are pipelined by inserting the throughput in the cost function of a tool based on simulated annealing. The results obtained on a series of benchmarks are then validated using a simple router that breaks long interconnects by suitably placing flip-flops along the wires