In this paper, we present FastPlace-a fast, iterative, flat placement algorithm for large-scale standard cell designs. FastPlace is based on the quadratic placement approach. The quadratic approach formulates the wirelength minimization problem as a convex quadratic program that can be solved efficiently by some analytical techniques. However it suffers from some drawbacks. First, the resulting placement has a lot of overlap among cells. Second, the resulting total wirelength may be long as the quadratic wirelength objective is only an indirect measure of the linear wirelength. Third, existing net models tend to create a lot of nonzero entries in the connectivity matrix that slows down the quadratic program solver. To handle the above problems we propose: 1) an efficient cell shifting technique to remove cell overlap from the quadratic program solution and also accelerate the convergence of the solver. This technique produces a global placement with even cell distribution in a very short time; 2) an iterative local refinement technique to reduce the wirelength according to the half-perimeter measure; and 3) a hybrid net model that is a combination of the traditional clique and star models. This net model greatly reduces the number of nonzero entries in the connectivity matrix and results in a significant speedup of the solver. Experimental results show that FastPlace is on average 13.4/spl times/,102/spl times/, and 19.9/spl times/ faster than state-of-the art academic placers Capo, Dragon, and Gordian-Domino, respectively, on a set of IBM benchmarks.

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FastPlace: efficient analytical placement using cell shifting, iterative local refinement,and a hybrid net model

This book provides broad and comprehensive coverage of the entire EDA flow. EDA/VLSI practitioners and researchers in need of fluency in an "adjacent" field will find this an invaluable reference to the basic EDA concepts, principles, data structures, algorithms, and architectures for the design, verification, and test of VLSI circuits. Anyone who needs to learn the concepts, principles, data structures, algorithms, and architectures of the EDA flow will benefit from this book.


Covers complete spectrum of the EDA flow, from ESL design modeling to logic/test synthesis, verification, physical design, and test - helps EDA newcomers to get "up-and-running" quickly 
Includes comprehensive coverage of EDA concepts, principles, data structures, algorithms, and architectures - helps all readers improve their VLSI design competence 
Contains latest advancements not yet available in other books, including Test compression, ESL design modeling, large-scale floorplanning, placement, routing, synthesis of clock and power/ground networks - helps readers to design/develop testable chips or products 
Includes industry best-practices wherever appropriate in most chapters - helps readers avoid costly mistakes



Table of Contents


Chapter 1: Introduction Chapter 2: Fundamentals of CMOS Design Chapter 3: Design for Testability Chapter 4: Fundamentals of Algorithms Chapter 5: Electronic System-Level Design and High-Level Synthesis Chapter 6: Logic Synthesis in a Nutshell Chapter 7: Test Synthesis Chapter 8: Logic and Circuit Simulation Chapter 9:?Functional Verification Chapter 10: Floorplanning Chapter 11: Placement Chapter 12: Global and Detailed Routing Chapter 13: Synthesis of Clock and Power/Ground Networks Chapter 14: Fault Simulation and Test Generation.

Electronic Design Automation: Synthesis, Verification, and Test

In this paper, we present FastPlace -- a fast, iterative, flat placement algorithm for large-scale standard cell designs. FastPlace is based on the quadratic placement approach. The quadratic approach formulates the wirelength minimization problem as a convex quadratic program, which can be solved efficiently by some analytical techniques. However it suffers from some drawbacks. First, the resulting placement has a lot of overlap among cells. Second, the resulting total wirelength may be long as the quadratic wirelength objective is only an indirect measure of the linear wirelength. Third, existing net models tend to create a lot of non-zero entries in the connectivity matrix, which slows down the quadratic program solver. To handle the above problems we propose: (1) An efficient Cell Shifting technique to remove cell overlap from the quadratic program solution and produce a global placement with even cell distribution. (2) An Iterative Local Refinement technique, to reduce the wirelength according to the half-perimeter measure. (3) A Hybrid Net Model which is a combination of the traditional clique and star models. This net model greatly reduces the number of non-zero entries in the connectivity matrix and results in a significant speedup of the solver. Experimental results show that FastPlace is on average 13.0 and 97.4 times faster than Capo and Dragon respectively. Correspondingly, the average wirelength is just 1.0% and 1.6% higher.

FastPlace: efficient analytical placement using cell shifting, iterative local refinement and a hybrid net model

Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research1, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning. In under six hours, our method automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area. To achieve this, we pose chip floorplanning as a reinforcement learning problem, and develop an edge-based graph convolutional neural network architecture capable of learning rich and transferable representations of the chip. As a result, our method utilizes past experience to become better and faster at solving new instances of the problem, allowing chip design to be performed by artificial agents with more experience than any human designer. Our method was used to design the next generation of Google’s artificial intelligence (AI) accelerators, and has the potential to save thousands of hours of human effort for each new generation. Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields. Machine learning tools are used to greatly accelerate chip layout design, by posing chip floorplanning as a reinforcement learning problem and using neural networks to generate high-performance chip layouts.

A graph placement methodology for fast chip design

Given the significance of placement in IC physical design, extensive research studies performed over the last 50 years addressed numerous aspects of global and detailed placement. The objectives and the constraints dominant in placement have been revised many times over, and continue to evolve. Additionally, the increasing scale of placement instances affects the algorithms of choice for high-performance tools. We survey the history of placement research, the progress achieved up to now, and outstanding challenges.

Progress and challenges in VLSI placement research

In this paper, we introduce an efficient analysis method for the power network of general topology. The new approach is based on algebraic multigrid (AMG) method that can avoid the slow convergence of basic iterative methods. An innovative adaptive coarsening and error-smoothing scheme is employed to further speed up the performance, taking advantage of the spatial variation of power supply noise. Experimental results show that our method is more than 100 times faster than SPICE3.

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Power network analysis using an adaptive algebraic multigrid approach

An efficient matrix solver is critical to the analytical placement. As the size of the matrix becomes huge, the multilevel methods turn out to be more efficient and more scalable. Algebraic Multigrid (AMG) is a multilevel technique to speedup the iterative matrix solver in K. Stuben (1999). We apply the algebraic multigrid method to solve the linear equations that arise from the analytical placement. A layout based clustering scheme is put forward to generate coarsening levels for the multigrid method. The experimental results show that the algebraic multigrid solver is promising for analytical placement.

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An algebraic multigrid solver for analytical placement without layout based clustering

In this paper, we introduce an efficient transistor-level simulation tool with SPICE-accuracy for deep-submicrometer very large-scale integration circuits with strong-coupling effects. The new approach uses multigrid for huge networks of power/ground, clock, and interconnect with strong coupling. Mutual inductance can be incorporated without error-prone matrix sparsification approximations or expensive matrix inversion. Transistor devices are integrated using a novel two-stage Newton-Raphson method to dynamically model the linear network and nonlinear devices boundary. Orders-of-magnitude speedup over Berkeley SPICE3 is observed for sets of real deep-submicrometer design circuits

Two-Stage Newton–Raphson Method for Transistor-Level Simulation

In this paper, we introduce a general operator splitting method for transient simulation of VLSI circuits. The proposed approach generates special partitions of the circuits and alternates the explicit and implicit integrations between the partitions. We prove that the method is unconditionally stable independent of the step size. The splitting scheme greatly reduces the nonzero fill-ins generated in direct methods like LU decomposition. Orders of magnitude speedup over Berkeley SPICE3 is observed for sets of circuits.

/pdf/an-unconditional-stable-general-operator-splitting-method-145yutijp8.pdf

An unconditional stable general operator splitting method for transistor level transient analysis

In this paper, we introduce an efficient transistor level simulation tool with SPICE-accuracy for deep-submicron (DSM) VLSI circuits with strong coupling effects. The new approach uses multigrid for large networks of power/ground, clock and signal interconnect. Transistor devices are integrated using a novel two-stage Newton-Raphson method to dynamically model the linear network and nonlinear devices interface. Orders of magnitude speedup over Berkeley SPICE3 is observed for sets of DSM design circuits.

Zhengyong Zhu

Papers

Power network analysis using an adaptive algebraic multigrid approach

An algebraic multigrid solver for analytical placement without layout based clustering

Two-Stage Newton–Raphson Method for Transistor-Level Simulation

An unconditional stable general operator splitting method for transistor level transient analysis

Efficient transient simulation for transistor-level analysis