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Особенности Data Flow Accelerator:
In computer programming , dataflow programming is a programming paradigm that models a program as a directed graph of the data flowing between operations, thus implementing dataflow principles and architecture. Dataflow programming languages share some features of functional languages , and were generally developed in order to bring some functional concepts to a language more suitable for numeric processing. Some authors use the term datastream instead of dataflow to avoid confusion with dataflow computing or dataflow architecture , based on an indeterministic machine paradigm. Traditionally, a program is modelled as a series of operations happening in a specific order; this may be referred to as sequential,  : p. The program focuses on commands, in line with the von Neumann  : p.
Plasticine: A Reconfigurable Dataflow Architecture for Software 2.0, Kunle Olukotun, Stanford-Part 1:
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Design for Highly Flexible and Energy-Efficient Deep Neural Network Accelerators [Yu-Hsin Chen]:
Also known as DFD, Data flow diagrams are used to graphically represent the flow of data in a business information system. DFD describes the processes that are involved in a system to transfer data from the input to the file storage and reports generation. Data flow diagrams can be divided into logical and physical. The logical data flow diagram describes flow of data through a system to perform certain functionality of a business. The physical data flow diagram describes the implementation of the logical data flow. Need to create Data Flow Diagram?
This processor provides data processing based on data-flow instructions rather than control flow instructions. As a result, during an execution on the accelerator of the Data-Flow Soft-Core, both partial data and instructions are eliminated as traffic for load and store activities. Data-flow instructions serve to describe a program and to dynamically change the context of a data-flow program graph inside the accelerator, on-the-fly. Our proposed design aims at combining the performance of a fine-grained data-flow architecture with the flexibility of reconfiguration, without requiring a partial reconfiguration or new bit-stream for reprogramming it. The potential of the data-flow implementation of a function or functional program can be exploited simply by relying on its description through the data-flow instructions that reprogram the Data-Flow Soft-Core. Moreover, the data streaming process will mirror those present in other FPGA applications. Finally, we show the advantages of this approach by presenting two test cases and providing the quantitative and numerical results of our evaluations. There is still a slight inclination of part of the High-Performance Computing HPC community to embrace the data-flow ideas in order to speed up the execution of scientific applications.