Dataflow Computation for the J-Machine

The dataflow model of computation exposes and exploits parallelism in programs without requiring programmer annotation; however, instruction- level dataflow is too fine-grained to be efficient on general-purpose processors. A popular solution is to develop a "hybrid'' model of computation where regions of dataflow graphs are combined into sequential blocks of code. I have implemented such a system to allow the J-Machine to run Id programs, leaving exposed a high amount of parallelism --- such as among loop iterations. I describe this system and provide an analysis of its strengths and weaknesses and those of the J-Machine, along with ideas for improvement.

Neural Networks

We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models.