Developments and experimental evaluation of partitioning algorithms for adaptive computing systems

Multi-FPGA systems offer the potential to deliver higher performance solutions than traditional computers for some low-level computing tasks. This requires a flexible hardware substrate and an automated mapping system. CHAMPION is an automated mapping system for implementing image processing applications in multi-FPGA systems under development at the University of Tennessee. CHAMPION will map applications in the Khoros Cantata graphical programming environment to hardware. The work described in this dissertation involves the automation of the CHAMPION backend design flow, which includes the partitioning problem, netlist to structural VHDL conversion, synthesis and placement and routing, and host code generation. The primary goal is to investigate the development and evaluation of three different k-way partitioning approaches. In the first and the second approaches, we discuss the development and implementation of two existing algorithms. The first approach is a hierarchical partitioning method based on topological ordering (HP). The second approach is a recursive algorithm based on the Fiduccia and Mattheyses bipartitioning heuristic (RP). We extend these algorithms to handle the multiple constraints imposed by adaptive computing systems. We also introduce a new recursive partitioning method based on topological ordering and levelization (RPL). In addition to handling the partitioning constraints, the new approach efficiently addresses the problem of minimizing the number of FPGAs used and the amount of computation, thereby overcoming some of the weaknesses of the HP and RP algorithms

Cycle time optimization by timing driven placement with simultaneous netlist transformations

We present new concepts to integrate logic synthesis and physical design. Our methodology uses general Boolean transformations as known from technology-independent synthesis, and a recursive bi-partitioning placement algorithm. In each partitioning step, the precision of the layout data increases. This allows effective guidance of the logic synthesis operations for cycle time optimization. An additional advantage of our approach is that no complicated layout corrections are needed when the netlist is changed

Automatic mapping of graphical programming applications to microelectronic technologies

Adaptive computing systems (ACSs) and application-specific integrated circuits (ASICs) can serve as flexible hardware accelerators for applications in domains such as image processing and digital signal processing. However, the mapping of applications onto ACSs and ASICs using the traditional methods can take months for a hardware engineer to develop and debug. In this dissertation, a new approach for automatic mapping of software applications onto ACSs and ASICs has been developed, implemented and validated. This dissertation presents the design flow of the software environment called CHAMPION, which is being developed at the University of Tennessee. This environment permits high-level design entry using the Cantata graphical programming software fromKRI. Using Cantata as the design entry, CHAMPION hides from the user the low-level details of the hardware architecture and the finer issues of application mapping onto the hardware. Validation of the CHAMPION environment was performed using multiple applications of moderate complexity. In one case, theapplication mapping time which required six weeks to perform manually took only six minutes for CHAMPION, yet comparable results were produced. Furthermore, the CHAMPION environment was constructed such that retargeting to a new adaptive computing system could be accomplished in just a few hours as opposed to weeks using manual methods. Thus, CHAMPION permits both ACSs and ASICs to be utilized by a wider audience and application development accomplished in less time

Flow-based Partitioning and Fast Global Placement in Chip Design

VLSI placement is one of the major steps in the chip design process and an interesting subject of research in industry and academia. Recent chips consist of several millions of circuits connected by millions of nets. The classical placement objective of finding positions for circuits and minimizing netlength among them is an ongoing issue in optimization of chip performance. The increasing instance sizes, the tightness of timing and routability constraints impose a real challenge to the design flows and the designers, which often cannot be addressed properly without considering them explicitly within the placement. Many of the complex design methodologies follow an iterative approach, using placement several times in this process. Thus, placement runtime has a severe impact on the turnaround time in chip development. The major contributios of this thesis deal with the global placement, a common relaxation of the placement problem, which computes rough positions of the circuits minimizing the total length of wires to interconnect the. Based on the idea of subsequent quadratic netlength minimization and partitioning, as in BonnPlace [BrennerStruzynaVygen:2008], we present several new algorithms, generalized data structures and a completely new implementation of this top-down placement scheme. We introduce and formalize the concept of movebounds which are position constraints on subsets of cells. Movebounds, which can be regarded as mandatory or soft constraints, provide a mechanism to explicitly incorporate movement constraints to the placement which result from issues of timing, power and routability. With inclusive movebounds, such restrictions can be assigned to groups of circuits without any influence to other placeable objects. The other constraints, namely the exclusive movebounds, are of particular interest for semi-hierarchical approaches, as they can be used to obtain a flat view of the design and prevent cells from being placed into hierarchy units. Both provide a toolbox to the designer and allow the control of particular circuit sets without netlist manipulations. We also present a top-down partitioning scheme and extend the legalization algorithm of [BrennerVygen:2004] to be able to deal with millions of cells and dozens of movebounds efficiently. The presented algorithm can handle different types of overlapping movebounds, even in legalization, and produces significantly better results than a modern industrial tool. We present a novel partitioning algorithm for global placement. Unlike previous iterative and recursive approaches, the new method provides a global view of the problem using a novel MinCostFlow model with extremely fast and highly parallelizable local realization steps. The new flow-based partitioning can address density targets much more accurately and lowers the risk of density violations. The presented MinCostFlow model does not depend on the number of cells, making it highly interesting for large and huge designs. Moreover, the embedded flow structure responds to the chip's floorplan much better than the classical global partitioning approach. Another significant advantage of this algorithm is the fact that it can be applied to any initial placement and guarantees a feasible (fractional) solution (if one exists), improving the tool's reliability, even with movebounds and starting from placements with significant density violations. Using this method we can extend the congestion-driven placement to a combined movement, density adjustment, and cell size inflation approach. This method is able to handle movebounds and guarantees to resolve density overloads properly. Flow-based partitioning creates the opportunity of applying local, density unaware, optimization steps within global placement and allows it to break the strict recursive structure of levels and save runtime. The extended flexibility and runtime improvement are not the only advantages. The proposed flow realization, which is a combination of local quadratic programs and local partitioning, does not only yield a runtime improvement, but also seems to merge connectivity information to partitioning in a much better way than the old recursive partitioning approach. The new flow-based partitioning helps to significantly improve the results of our placement also in terms of netlength. We provide fast data structures for hierarchically clustered netlists and extend the net models Clique and Star to be applied within the clustered netlists efficiently. We show how shared-memory parallelization can be used for speeding up various routines in placement, without the loss of repeatability. In addition, we commit ourselves to the clustering problem, finding circuit groups which should be placed in the vicinity of each other. In order to provide global information for a fast bottom-up clustering, we propose to incorporate connectivity information using random walks. To this end, we show how the hitting times can be efficiently retrieved from large netlist hypergraphs. Due to the proposed model, parallel computation on sparse, shared-memory matrices can be used for computing hitting times to several targets simultaneously. Combined with a bottom-up clustering, even our preliminary approach significantly outperforms the popular BestChoice} algorithm [Nam et al. 2005]. We conclude this thesis by providing several experimental results on a large testbed of real-world chips and benchmarks demonstrating the performance of our tool. Without movebounds, our tool performs as good as a state-of-the-art force directed placer, but is more than 5x faster. We achieve the same speedup over the old BonnPlace, but produce significantly better results, on average more than 8%. With movebounds, our placements are more than 30% shorter compairing to the force-directed placer and our tool is 9x-20x faster. Our tool also produces the best results on the latest ISPD 2006 placement benchmarks

Partitioning a given circuit targeting multiple Fpgas

Our approach to the problem of partitioning the design (represented as a hypergraph) into Multi-FPGAs uses a bi-level approach by initially clustering the design and then applying the bipartitioning technique iteratively. Each partition generated by the iterative bipartitioning technique should meet the constraints given by the FPGAs input-output and number of CLBs. The traditional FM partitioning can be applied to partition the circuit into multiple FPGAs. FM partitioning aims to minimize the number of interconnections but fails to group the nodes with maximum interconnections into one partition. Thus FM algorithm looks at the partitioning problem with a global viewpoint, abandoning the details. The proposed algorithm adds another level of optimization to the partitioning heuristic. By clustering the nodes that are connected very closely in a netlist before partitioning, local optimization property is added to the FM algorithm. This clustered circuit is then partitioned to implement the design in multiple FPGAs. Bipartitioning using the Fiduccia Mattheyses algorithm is applied. (Abstract shortened by UMI.)

Impact of Multi-level Clustering on Performance Driven Global Placement

Delay and wirelength minimization continue to be important objectives in the design of high-performance computing systems. For large-scale circuits, the clustering process becomes essential for reducing the problem size. However, to the best of our knowledge, there is no study about the impact of multi-level clustering on performance-driven global placement. In this paper, five clustering algorithms including the quasi-optimal retiming delay driven PRIME and the cutsize-driven ESC have been considered for their impact on state-of-the-art mincut based global placement. Results show that minimizing cutsize or wirelength during clustering typically results in significant performance improvements