Research on Massively Parallel Autonomous Multi-Processor System and Neural Network

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In this research, we have proposed a massively parallel Autonomous Multi-Processor (AMP) for computer vision and neural networks. the system has about 10^5 processor elements (PE's) connected to one another via communication network. Each PE runs asynchronously based on a data flow control mechanism. In the data flow control mechanism, all operations are executed in parallel, according to data dependency, whether they are executed in the same processor or not. This simplifies to implement neural networks and computer vision models in a massively parallel way.In this research, we have estimated the performance of the AMP system using a software simulator which simulates the system at the register transfer level. This shows that the system provides a high computation performance based on the high parallelism for not only low level image processing but for neural networks and intermediate level image processing. Also we have proposed a programming language based on functional programming language, which improves the software productivity for large scale applications.