Efficient circuit partitioning algorithms for parallel logic simulation

Graphics Processing Units (GPUs) are gaining popularity for parallelization of general purpose applications. GPUs are massively parallel processors with huge performance in a small and readily available package. At the same time, the emergence of general purpose programming environments for GPUs such as CUDA shorten the learning curve of GPU programming. We present a GPU-based parallelization of logic simulation algorithm for electronic designs. Logic simulation is a crucial component of verification of electronic designs that allows one to check whether the design behaves according to the specifications. Verification of electronic designs consumes more than 60% of the overall design cycle. Any attempts to speedup the verification process (and logic simulation) results in great savings and shorter time-to-market. We develop a parallel cycle-based logic simulation algorithm that uses And Inverter Graphs (AIGs) as design representations and exploits the massively parallel GPU architecture. We demonstrate several orders of speedups on benchmarks using our system.

Optimizing highway alignments through complex environments is a difficult problem. Optimal alignment is influenced by many factors and should be based on trade-offs among various supplier and user cost components. Previous works on alignment optimization usually have neglected some important components of the total cost function that should influence the optimal solution. The formulations of different cost components for two-lane rural highways are presented. The proposed cost functions are based on the geometric features of both horizontal and vertical alignments. The cost formulations can be employed in models for simultaneously optimizing three-dimensional alignments. Possible methods for solving such optimization problems are provided. A numerical example also is presented to illustrate the optimization of three-dimensional alignments with the proposed cost functions.

We propose a new scheme of subband adaptive digital filter(ADF) which allows massive parallel processing. We discuss the power spectral density of the down-sampled processes and propose a new cost function which is suitable for subband ADF. The subband scheme of adaptive system identification using the proposed cost function is considered. It is shown that we can carry out precise system identification based on the frequency domain sampling theorem, in which the proposed cost function is applied to the subband ADF using polyphase filter banks. The cost function is defined in each bin; as a result, it enables the perfect parallel processing of ADFs. >

In this paper we present a new method for the determination of geometrical misalignments in cone-beam CT scanners, from the analysis of the projection data of a generic object. No a priori knowledge of the object shape and positioning is required. We show that a cost function, which depends on the misalignment parameters, can be defined using the projection data and that such a cost function has a local minimum in correspondence to the actual parameters of the system. Hence, the calibration of the scanner can be carried out by minimizing the cost function using standard optimization techniques. The method is developed for a particular class of 3D object functions, for which the redundancy of the fan beam sinogram in the transaxial midplane can be extended to cone-beam projection data, even at wide cone angles. The method has an approximated validity for objects which do not belong to that class; in that case, a suitable subset of the projection data can be selected in order to compute the cost function. We show by numerical simulations that our method is capable to determine with high accuracy the most critical misalignment parameters of the scanner, i.e., the transversal shift and the skew of the detector. Additionally, the detector slant can be determined. Other parameters such as the detector tilt, the longitudinal shift and the error in the source–detector distance cannot be determined with our method, as the proposed cost function has a very weak dependence on them. However, due to the negligible influence of these latter parameters in the reconstructed image quality, they can be kept fixed at estimated values in both calibration and reconstruction processes without compromising the final result. A trade-off between computational cost and calibration accuracy must be considered when choosing the data subset used for the computation of the cost function. Results on real data of a mouse femur as obtained with a small animal micro-CT are shown as well, proving the capability of the proposed calibration method. In principle, the method can be adapted to other cone-beam imaging modalities (e.g., single photon emission computed tomography).

Traditional Discrete Wavelet Transform (DWT) image compression fails to reserve texture information. JPEG2000 part 2 extends to allow arbitrary subband decomposition and different decomposition direction. In this paper, we aim to provide a suitable wavelet packet basis selection for JPEG2000 by the improved cost function. Various cost functions used to search for the optimal wavelet basis are discussed and compared, and the estimated coding cost (ECC) is chosen as our criterion to achieve both accuracy and simplified computation. Then the entropy coding scheme of JPEG2000 is analyzed in detail and modeled to get our cost function. Experimental results show that compared with other cost function, the proposed cost function with ECC is more efficient in terms of PSNR (Peak Signal to Noise Rate). The proposed adaptive wavelet packet coding can provides higher visual quality than original JPEG2000.

The significantly increased density of pins of stan-dard cells and the reduced number of routing tracks at sub-10nm nodes have made the pin access problem in detailed routing very difficult. To alleviate this pin accessibility problem in detailed routing, recent works have proposed to make a small perturbation of cell shifting, cell flipping, and adjacent cells swapping in the detailed placement stage. Here, an essential element for the success of pin accessibility aware detailed placement is the installed cost function, which should be sufficiently accurate in predicting the degree of routing difficulty in accessing pins. In this work, we propose a new model of cost function that is comprehensively devised to overcome the limitations of the prior ones. Precisely, unlike the conventional cost functions, our proposed cost function model is based on the empirical routing data in order to fully reflect the potential outcomes of detailed routing. Through experiments with benchmark circuits, it is shown that using our proposed cost function in detailed placement is able to reduce the routing errors by 44 % on average while using the existing cost functions reduce the routing errors on average by at most 15 %.

In this paper we propose a new feature extraction scheme for hyperspectral images based on mutual information. Relevance of extracted feature set to class label has been measured by average of mutual information between each of them and class label and Redundancy of them is measured by average of mutual information between each pair of them. Based on relevance of features and redundancy between them, we propose a cost function that maximize relevance of extracted features and simultaneously minimize redundancy between them. This cost function has been already used for feature selection. In this paper we will find the parameters of an optimal linear mapping by optimizing the proposed cost function with respect them. Linear methods are attractive due to their simplicity. Because of nonlinear and nonconvex relation between proposed cost function and the parameters, we use genetic algorithm for optimization. Mutual information accounts for higher order statistics, not just for second order as PCA and LDA do. Hence mutual information is a better criterion for hyperspectral images because they have higher order statistics than two. Our classification results for AVARIS data shows proposed method has better performance over PCA and LDA.

This paper discusses the problem of local path planning in a static-obstacle environment by designing a PSO-based receding horizon control approach. In order to avoid obstacles, a virtual robot is first designed and moves along the boundary of obstacles. Then, in the framework of receding horizon control, a cost function is proposed where the virtual robot and the target position are integrated, which implies that mobile robots are controlled to keep a security distance and velocity consensus with virtual robots, and to move toward the target position. Next, the proposed cost function with constraints is processed by a particle swarm optimization (PSO) algorithm such that the PSO-based receding horizon control approach is developed. By solving the proposed cost function, a control sequence is obtained and then the first control input is used to enable the robot toward the target and avoid obstacles. Finally, the performance capabilities of the PSO-based receding horizon control approach are illustrated by simulation results.

In this paper, a robust face recognition algorithm is proposed, which is based on the elastic graph matching (EGM) and discriminative feature analysis algorithm. We introduce a cost function for the EGM taking account of variations in face pose and facial expressions, and propose its optimization procedure. Our proposed cost function uses a set of Gabor-wavelet-based features, called robust jet, which are robust against the variations. The robust jet is defined in terms of discrete Fourier transform coefficients of Gabor coefficients. To cope with the difference between face poses of test face and reference faces, 2 x 2 warping matrix is incorporated in the proposed cost function. For the discriminative feature analysis, linear projection discriminant analysis and kernel-based projection discriminant analysis are introduced. These methods are motivated to solve the small-size problem of training samples. The basic idea of PDA is that a class is represented by a subspace spanned by some training samples of the class instead of using sample mean vector, that the distance from a pattern to a class is defined by using the error vector between the pattern and its projection to the subspace representing the class, and that an optimum feature selection rule is developed using the distance concept in a similar way as in the conventional linear discriminant analysis. In order to evaluate the performance of our face recognition algorithm, we carried out some experiments using the well-known FERET face database, and compared the performance with recently developed approaches. We observed that our algorithm outperformed the compared approaches.

A fast algorithm for fault simulation, using data-level parallelism, is presented for the Connection Machine. The algorithm is of the parallel pattern single-fault-propagation type. An implementation in C language has been completed and results are presented. >

ABSTRACTMesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among proces-sors on a parallel machine. We present a novel method todynamically balance the processor workloads with a globalview. This paper presents, for the first time, the imple-mentation and integration of all major components withinour dynamic load balancing strategy for adaptive grid cal-culations. Mesh adaption, repartitioning, processor assign-ment, and remapping are critical components of the frame-work that must be accomplished rapidly and efficiently soas not to cause a significant overhead to the numerical sim-ulation. Previous results indicated that mesh repartitioningand data remapping are potential bottlenecks for perform-ing large-scale scientific calculations. We resolve these issuesand demonstrate that our framework remains viable on alarge number of processors.1 INTRODUCTIONDynamic mesh adaption on unstructured grids is a power-ful tool for computing unsteady three-dimensional problemsthat require grid modifications to efficiently resolve solu-tion features. By locally refining and coarsening the meshto capture flowfield phenomena of interest, such proceduresmake standard computational methods more cost effective.Highly refined meshes are required to accurately captureshock waves, contact discontinuities, vortices, and shear lay-ers. Local mesh adaption provides the opportunity to obtainsolutions that are comparable to those obtained on globally-refined grids but at a much lower cost.Unfortunately, the adaptive solution of unsteady prob-lems causes load imbalance among processors on a paral-lel machine. This is because the computational intensity isboth space and time dependent. An efficient parallel imple-mentation of such methods is extremely difficult to achieve,primarily because of the dynamically-changing nonuniformgrid. Various methods on dynamic load balancing have beenreported to date [5-9,11-14,16-18,24-26]; however, most ofthem either lack a global view of loads across processors orProceedings of the 9th ACM Symposium on Parallel Al-gorithms and Architectures, Newport, Rhode Island, June22-25, 1997.do not apply their techniques to realistic large-scale appli-cations.Figure 1 depicts our framework for parallel adaptive flowcomputation. It consists of a flow solver and a mesh adap-tor, with a partitioner and a remapper that load balancesand redistributes the computational mesh when necessary.Our goal is to build a portable system for efficiently per-forming adaptive large-scale flow calculations in a parallelmessage-passing environment. The mesh is first partitionedand mapped among the available processors. The flow solverthen runs for several iterations, updating solution variables.Once an acceptable solution is obtained, the mesh adaptionprocedure is invoked. It first targets edges for coarseningand refinement based on an error indicator computed fromthe flow solution. The old mesh is then coarsened, resultingin a smaller grid. Since edges have already been marked forrefinement, it is possible to exactly predict the new meshbefore actually performing the refinement step. Programcontrol is thus passed to the load balancer at this time. Aquick evaluation step determines if the new mesh will be sounbalanced as to warrant a repartitioning. If the currentpartitions will remain adequately load balanced, control mpassed back to the subdivision phase of the mesh adaptor.Otherwise, a repartitioning procedure is used to divide thenew mesh into subgrids. The new partitions are then reas-signed to the processors in a way that minimizes the costof data movement. If the remapping cost is less than thecomputational gain that would be achieved with balancedpartitions, all necessary data is appropriately redistributed.Otherwise, the new partitioning is discarded. The computa-tional mesh is then actually refined and the flow calculationis restarted.Notice from the framework in Fig. 1 that splitting themesh refinement step into two distinct phases of edge mark-ing and mesh subdivision allows the subdivision phase tooperate in a more load balanced fashion. In addition, sincedata remapping is performed before the mesh grows in sizedue to refinement, a smaller volume of data is moved. Thiscan lead to a potentially significant savings in the redistribu-tion cost. The load balancer also balances the computationalload for the flow solver while reducing the runtime commu-nication. This is important because flow solvers are usuallyseveral times more expensive than mesh adaptors. In anycase, it is obvious that mesh adaption, repartitioning, pro-cessor assignment, and remapping are critical componentsof the framework and must be accomplished rapidly and ef-ficiently so as not to cause a significant overhead to the flowcomputation.

Virtual network embedding has been gaining interest due to the diverse applications of virtual networks. Especially building a cooperative testbed for future network design requires highly efficient virtual network embedding There have been many virtual network embedding algorithms proposed so far. However, these algorithms assume simple cost functions such as minimizing the maximum utilization or maximizing the number of virtual networks embedded in the physical network under given constraints. In this paper, we propose to use a more realistic cost function so that it can take into account diverse aspects of costs in the virtual network embedding Furthermore, we propose a Greedy embedding algorithm that is suitable for the proposed cost function. Through extensive simulation analysis, we show that our proposed Greedy approach performs much better compared to a Random approach. We also show that the performance is similar to a theoretical lower bound for small to medium resource demands.

Quantum-dot cellular automata (QCA) is potentially a very attractive alternative to CMOS for future digital designs. Circuit designs in QCA have been extensively studied. However, how to properly evaluate the QCA circuits has not been carefully considered. To date, metrics and area-delay cost functions directly mapped from CMOS technology have been used to compare QCA designs, which is inappropriate due to the differences between these two technologies. In this paper, several cost metrics specifically aimed at QCA circuits are studied. It is found that delay, the number of QCA logic gates, and the number and type of crossovers, are important metrics that should be considered when comparing QCA designs. A family of new cost functions for QCA circuits is proposed. As fundamental components in QCA computing arithmetic, QCA adders are reviewed and evaluated with the proposed cost functions. By taking the new cost metrics into account, previous best adders become unattractive and it has been shown that different optimization goals lead to different “best” adders.

This paper presents the design of a continuous control set model predictive control (CCS-MPC) for a permanent magnet synchronous machine (PMSM). The CCS-MPC is designed for the control of the stator currents of a PMSM following a similar scheme to the one introduced in field-oriented control (FOC). In the presented CCS-MPC, the proposed cost function is modified by the introduction of a set of orthogonal functions known as Laguerre functions. Furthermore, an exponential cost function is introduced to afford long horizon predictions. The control law is derived by the formulation of a constrained optimization problem. By the introduction of the Laguerre functions and the exponential cost function, the computational burden is reduced and a long prediction horizon can be accomplished. Simulation results on Matlab/Simulink are presented to verify the performance of the proposed methodology.

This paper addresses the problem of sensor selection in wireless sensor networks (WSN) subject to a distortion constraint. To do so, first, a cost function is derived based on the spatial correlation obtained using the best estimation of the event source. Then, a new adaptive algorithm is proposed in which the number of active sensors is adaptively determined and the best topology of the active set is selected based on the add-one-sensor-node-at-a-time method. Simulations results show that the active sensors selected using the proposed cost function have less event distortion. Also, it is shown that the proposed sensor selection algorithm is near optimum and it has better performance than other algorithms with regard to the computational burden and distortion.