8,631 research outputs found

    OPTIMAL AREA AND PERFORMANCE MAPPING OF K-LUT BASED FPGAS

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    FPGA circuits are increasingly used in many fields: for rapid prototyping of new products (including fast ASIC implementation), for logic emulation, for producing a small number of a device, or if a device should be reconfigurable in use (reconfigurable computing). Determining if an arbitrary, given wide, function can be implemented by a programmable logic block, unfortunately, it is generally, a very difficult problem. This problem is called the Boolean matching problem. This paper introduces a new implemented algorithm able to map, both for area and performance, combinational networks using k-LUT based FPGAs.k-LUT based FPGAs, combinational circuits, performance-driven mapping.

    Placement-Driven Technology Mapping for LUT-Based FPGAs

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    In this paper, we study the problem of placement-driven technology mapping for table-lookup based FPGA architectures to optimize circuit performance. Early work on technology mapping for FPGAs such as Chortle-d[14] and Flowmap[3] aim to optimize the depth of the mapped solution without consideration of interconnect delay. Later works such as Flowmap-d[7], Bias-Clus[4] and EdgeMap consider interconnect delays during mapping, but do not take into consideration the effects of their mapping solution on the final placement. Our work focuses on the interaction between the mapping and placement stages. First, the interconnect delay information is estimated from the placement, and used during the labeling process. A placement-based mapping solution which considers both global cell congestion and local cell congestion is then developed. Finally, a legalization step and detailed placement is performed to realize the design. We have implemented our algorithm in a LUT based FPGA technology mapping package named PDM (Placement-Driven Mapping) and tested the implementation on a set of MCNC benchmarks. We use the tool VPR[1][2] for placement and routing of the mapped netlist. Experimental results show the longest path delay on a set of large MCNC benchmarks decreased by 12.3 % on the average

    Optimal simultaneous mapping and clustering for FPGA delay optimization

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    Technology Mapping for Circuit Optimization Using Content-Addressable Memory

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    The growing complexity of Field Programmable Gate Arrays (FPGA's) is leading to architectures with high input cardinality look-up tables (LUT's). This thesis describes a methodology for area-minimizing technology mapping for combinational logic, specifically designed for such FPGA architectures. This methodology, called LURU, leverages the parallel search capabilities of Content-Addressable Memories (CAM's) to outperform traditional mapping algorithms in both execution time and quality of results. The LURU algorithm is fundamentally different from other techniques for technology mapping in that LURU uses textual string representations of circuit topology in order to efficiently store and search for circuit patterns in a CAM. A circuit is mapped to the target LUT technology using both exact and inexact string matching techniques. Common subcircuit expressions (CSE's) are also identified and used for architectural optimization---a small set of CSE's is shown to effectively cover an average of 96% of the test circuits. LURU was tested with the ISCAS'85 suite of combinational benchmark circuits and compared with the mapping algorithms FlowMap and CutMap. The area reduction shown by LURU is, on average, 20% better compared to FlowMap and CutMap. The asymptotic runtime complexity of LURU is shown to be better than that of both FlowMap and CutMap

    Throughput-driven floorplanning with wire pipelining

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    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

    Clustering-Based Simultaneous Task and Voltage Scheduling for NoC Systems

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    Network-on-Chip (NoC) is emerging as a promising communication structure, which is scalable with respect to chip complexity. Meanwhile, latest chip designs are increasingly leveraging multiple voltage-frequency domains for energy-efficiency improvement. In this work, we propose a simultaneous task and voltage scheduling algorithm for energy minimization in NoC based designs. The energy-latency tradeoff is handled by Lagrangian relaxation. The core algorithm is a clustering based approach which not only assigns voltage levels and starting time to each task (or Processing Element) but also naturally finds voltage-frequency clusters. Compared to a recent previous work, which performs task scheduling and voltage assignment sequentially, our method leads to an average of 20 percent energy reduction

    FPGA technology mapping optimizaion by rewiring algorithms.

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    Tang Wai Chung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 40-41).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Rewiring Algorithms --- p.3Chapter 2.1 --- REWIRE --- p.5Chapter 2.2 --- RAMFIRE --- p.7Chapter 2.3 --- GBAW --- p.8Chapter 3 --- FPGA Technology Mapping --- p.11Chapter 3.1 --- Problem Definition --- p.13Chapter 3.2 --- Network-flow-based Algorithms for FPGA Technology Mapping --- p.16Chapter 3.2.1 --- FlowMap --- p.16Chapter 3.2.2 --- FlowSYN --- p.21Chapter 3.2.3 --- CutMap --- p.22Chapter 4 --- LUT Minimization by Rewiring --- p.24Chapter 4.1 --- Greedy Decision Heuristic for LUT Minimization --- p.27Chapter 4.2 --- Experimental Result --- p.28Chapter 5 --- Conclusion --- p.38Bibliography --- p.4
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