Learn More
We present a novel method to efficiently generate, compress and apply test patterns in a logic BIST architecture. Patterns are generated by a modified automatic test pattern generator (ATPG) and are encoded as linear feedback shift register (LFSR) initial values (seeds); one or more patterns can be encoded into a single LFSR seed. During test application,(More)
This paper presents a fast and scalable parallel algorithm for volume rendering and its implementation on distributed-memory parallel computers. This parallel algorithm is based on the shear-warp algorithm of Lacroute and Levoy. Coupled with optimizations that exploit coherence in the volume and image space, the shear-warp algorithm is currently(More)
We present a new multiprocessor sequential circuit fault simulator, Zamlog, based on a novel uniprocessor simulator, Zambezi. Both the fault and test sets are partitioned for multiprocessor simulation. The parallelization technique, designed to preserve the efficiency of Zambezi, is simple to implement and has low communication requirements. Experimental(More)
In this paper, we present a new technique for mapping the backpropagation algorithm on hypercubes and related architectures. A key component of this technique is a network partitioning scheme which is called checkerboarding. Checkerboarding allows us to replace the all-to-all broadcast operation performed by the commonly used vertical network partitioning(More)
Fault simulation is a compute-intensive problem. Data parallel simulation on multiple processors is one method to reduce fault simulation time. In this paper , we discuss a novel technique to partition the fault set for data parallel fault simulation. When applied statically, the technique can scale well for up to eight processors. The fault set(More)
relational database, parallel join algorithm, load balancing, adaptive, main memory database, workstation cluster Many parallel join algorithms have been proposed in the last several years. However, most of these algorithms require that the amount of data to be joined is known in advance in order to choose the proper number of join processors. This is an(More)
Neural networks have traditionally been applied to recognition problems, and most learning algorithms are tailored to those problems. We discuss the requirements of learning for generalization , where the traditional methods based on gradient descent have limited success. We present a new stochastic learning algorithm based on simulated annealing in weight(More)