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2016•Unpublished venueRequires access

Implementation of perception algorithm using DataFlow paradigm

Miloš Kotlar, Zoran Babović, Veljko Milutinović

Open publisher page 1 citations

Abstract

This paper presents an implementation of Perceptron algorithm using DataFlow approach. DataFlow is a new paradigm that is suitable for solving BigData problems in applications. The iterative nature of the algorithm and a large training set make it suitable for implementing on DataFlow accelerators. Also we discuss differences between ControlFlow paradigm, which is based on the von Neumann architecture, and the DataFlow paradigm, which is based on the Feynman architecture and relies on reconfigurable FPGA chips. We compare these paradigms through aspects such as speed, power dissipation and size. We provide the implementation of Perceptron algorithm using DataFlow hardware accelerator. Results show that DataFlow implementation of Perceptron algorithm is 6 times faster than the implementation on the single core Intel CPU.

About this research paper

What this paper is about

This paper presents an implementation of Perceptron algorithm using DataFlow approach. DataFlow is a new paradigm that is suitable for solving BigData problems in applications. The iterative nature of the algorithm and a large training set make it suitable for implementing on DataFlow accelerators. Also we discuss differences between ControlFlow paradigm, which is based on the von Neumann architecture, and the DataFlow paradigm, which is based on the Feynman architecture and relies on reconfigurable FPGA chips. We compare these paradigms through aspects such as speed, power dissipation and size. We provide the implementation of Perceptron algorithm using DataFlow hardware accelerator. Results show that DataFlow implementation of Perceptron algorithm is 6 times faster than the implementation on the single core Intel CPU.

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Available abstract

This paper presents an implementation of Perceptron algorithm using DataFlow approach. DataFlow is a new paradigm that is suitable for solving BigData problems in applications. The iterative nature of the algorithm and a large training set make it suitable for implementing on DataFlow accelerators. Also we discuss differences between ControlFlow paradigm, which is based on the von Neumann architecture, and the DataFlow paradigm, which is based on the Feynman architecture and relies on reconfigurable FPGA chips. We compare these paradigms through aspects such as speed, power dissipation and size. We provide the implementation of Perceptron algorithm using DataFlow hardware accelerator. Results show that DataFlow implementation of Perceptron algorithm is 6 times faster than the implementation on the single core Intel CPU.

Key concepts: Dataflow, Dataflow architecture, Computer science, Parallel computing, Algorithm, Field-programmable gate array, Perceptron, Computer architecture

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