Метод оценки ресурсов в процессе функционально-потокового высокоуровневого синтеза СБИС

Abstract

The problems of high-level synthesis of very large integrated circuits (VLSI) are considered. The review of the subject area shows that the use of the imperative model and corresponding programming languages does not provide efficient parallelization of algorithms and the possibility of efficient parallelization of programs. This leads to the impossibility of providing the required technical characteristics. This is due to the specifics of VLSI, which is essentially a scheme of parallel processing of information flows. An original VLSI synthesis method is presented. The method based on the functional-streaming paradigm of parallel computing. This method allows ensuring architectural independence and maximum coverage of implementation options. The route map of VLSI functional-flow method is outlined. The problem of estimating the requested hardware resources and clock frequency, necessary for solving, is formulated. This problem must be solved at the early stages of design. A method for estimating resources in the process of functional-flow synthesis is proposed. The method is based on the use of an additional meta-layer (HDL-graph). Taking into account the polymorphism of the solution of the resource estimation problem, it is proposed to use machine learning technologies in the new method. It is shown that the application of the indicated method in the synthesis process makes it possible to provide the most accurate assessment of resources. This is possible, because the HDL graph is a data flow graph typed and structured in accordance with the functional-flow model of parallel computing. Machine learning allows to most effectively obtain a solution to the problem of optimal selection of the required resources. The classes of resources for which an assessment is required are highlighted. Selected parameters for building a resource assessment model. The software implementation and comparison of the proposed resource estimation method based on linear regression models, neural networks and gradient boosting with known approaches is performed. It is shown that when using the technology of functional-flow synthesis when applying the proposed method for estimating the required resources and performance, an increase in the accuracy of the estimate at the high-level stage.