GPU based EDA optimization

In this paper we study the performance of Spiking Neural Networks (SNN)and Support Vector Machine (SVM) by using a GPU, model GeForce 6400M. Respect to applications of SNN, the methodology may be used for clustering, classification of databases, odor, speech and image recognition..In case of methodology SVM, is typically applied for clustering, regression and progression. According to particular characteristics of these methodologies,theycan be parallelizedin several grades. However, level of parallelism is limited to architecture of hardware. So, is very sure to get better results using other hardware with more computational resources. The different approaches are evaluated by the training speed and performance. On the other hand, some authors have coded algorithms SVM light, but nobody has programming QP SVM in a GPU. Algorithms were coded by authors in the hardware, like Nvidia card, FPGA or sequential circuits that depends on methodology used, to compare learning timewith between GPU and CPU. Also, in the survey we introduce a brief description of the types of ANN and its techniques of execution to be related with results of researching.

Efficient large-scale circuit simulation is among the most challenging problems facing the EDA industry today since it is the only feasible way to verify a circuit's behavior prior to manufacturing. In recent years, the emphasis has been placed on preconditioning methods that reduce the number of iterations for solving large Symmetric Diagonally Dominant systems resulting from the Modified Nodal Analysis. This paper presents a GPU-accelerated Preconditioned Conjugate Gradient (PCG) iterative method preconditioned by the Combinatorial Multigrid (CMG) for fast DC and transient analysis of large-scale linear circuits. Experimental results on IBM industrial power grids demonstrate speedups up to 4.69x and 4.50x for the PCG method and the CMG preconditioning algorithm, respectively, over the optimized CPU implementations.