An optimum application-specific data flow architecture for accelerating the standard event driven logic simulation is developed. A conservative distributed simulation algorithm which minimizes the use of NULL messages is also developed, along with a pseudodynamic data flow architecture to implement this distributed algorithm efficiently. A comparison of the standard event driven algorithm-based data flow accelerator to the distributed simulation algorithm-based accelerator is made on several benchmark circuits. The distributed simulation algorithm on the specialized data flow accelerator outperforms the standard event driven algorithm based data flow accelerator by a factor of three in most cases.>

Reprogrammable dataflow neural classifiers are proposed as an alternative to traditional implementations. In general, these classifiers are based on functional languages, neural-dataflow transformations, dataflow algorithmic transformations, and dataflow multiprocessors. An experimental approach is used to investigate the performance of a large-scale fine-grained dataflow classifier architecture. In this study, the functional descriptions of high level data dependency of a supervised learning algorithm are transformed into a machine executable low-level dataflow graph. The tagged token dataflow algorithmic transformation is applied to exploit the parallelism. Dataflow neural classifiers are used to implement the learning algorithm. No attempt is made to optimize the granularity of the high-level language programming blocks to balance the computation and communication. The proposed classifier architecture is more versatile than other existing architectures. Performance results show the effectiveness of dataflow neural classifiers.>

An experimental approach is chosen to investigate the performance of a fine-grained dataflow architecture for numerically intensive digital signal processing (DSP) applications. The focus is on the behavior of pipelined data-parallel algorithms. However, the granularity of the high-level language programming blocks is not explicitly optimized to balance computation and communication; a natural and logical fine-grained decomposition of problems is used instead. The authors interpret their empirical data by means of parameters such as a number of instructions per generic unit of computation, a density of precedence relations, and a serial fraction. The performance and limitations of fine-grained general-purpose dataflow computing are discussed.>

The authors compare dataflow computing models, languages, and dataflow computing machines for numerical and nonnumerical computations. The high-level-language-graph transformations that must be performed to achieve high performance for numerical and nonnumerical programs executed in a dataflow computing environment are described for Lisp, using the DCBL transformations. Some general problems of dataflow computing machines are discussed. Performance evaluation measurements obtained by executing benchmark programs in the ETL nonnumerical dataflow computing environment, the EM-3, are presented.>