Titlebook: Hardware Acceleration of EDA Algorithms; Custom ICs, FPGAs an Kanupriya Gulati,Sunil P. Khatri Book 2010 Springer-Verlag US 2010 FPGA.Field

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Hardware Platformstrends. With the growing complexity of VLSI designs, this is a significant problem for the electronic design automation (EDA) community. In addition to multi-core processors, hardware-based accelerators such as custom-designed ICs, reconfigurable hardware such as FPGAs, and streaming processors such

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GPU Architecture and the CUDA Programming Modelorce 8800 GTS devices, which are the GPUs used in our implementations. We discuss the hardware model, memory model, and the programmingmodel for these devices, in order to provide background for the reader to understand the GPU platform better.

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Accelerating Boolean Satisfiability on an FPGAmed in hardware, in parallel. In our approach, clause literals are stored in the FPGA slices. In order to solve large SAT instances, we heuristically partition the clauses into a number of ‘bins,’ each of which can fit in the FPGA. This is done in a preprocessing step.

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Accelerating Circuit Simulation Using Graphics Processorse has been significant interest in accelerating SPICE. Since a large fraction (on average 75%) of the SPICE runtime is spent in evaluating transistor model equations, a significant speedup can be availed if these evaluations are accelerated.

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Conclusionsng frequencies. This is attributed to the power, memory, and ILP walls that are encountered as VLSI technology scales. At the same time, microprocessors are becoming increasingly complex with multiple cores being implemented on the same IC.

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