I. N. Ryzhenko, O. V. Nepomnyaschy, A. I. Legalov, V. V. Shaidurov
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引用次数: 0
Abstract
In this paper, methods for increasing the efficiency of VLSI development based on the method of architecture-independent design are proposed. The route of high-level VLSI synthesis is considered. The principle of constructing a VLSI hardware model based on the functional-flow programming paradigm is stated. The results of the development of methods and algorithms for the transformation of functional-parallel programs into programs in HDL languages that support the design process of digital chips are presented. The principles of assessment are considered and the classes of resources required for the analysis of design solutions are identified. Reduction coefficients and methods of their calculation for each resource class are introduced. An algorithm for calculating the reduction coefficients and estimating the required resources is proposed. An algorithm for converting parallelism is proposed, taking into account the specified constraints of the target platform. A mechanism for the exchange of metrics with an architecture-dependent level is developed. Examples of the reduction of parallelism for the FPGA platform and practical implementation of FFT algorithms in the Virtex® UltraScale FPGA basis are given. The developed methods and algorithms make it possible to use the method of architecture-independent synthesis for transferring VLSI projects to various architectures by changing the parallelism of the circuit and equivalent transformations of parallel programs. The proposed approach provides many options for hardware solutions for implementation on various target platforms.
期刊介绍:
Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision