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J. Low Power Electron. Appl. 2017, 7(4), 29; https://doi.org/10.3390/jlpea7040029

Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing

Department of Computer Engineering, Missouri University of Science & Technology, 141 Emerson Electric Co. Hall, 301 W. 16th St, Rolla, MO, 65409-0040, USA
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Received: 6 September 2017 / Revised: 26 October 2017 / Accepted: 13 November 2017 / Published: 17 November 2017
(This article belongs to the Special Issue FPGA and Reconfigurable Computing)
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Abstract

The high performance of FPGA (Field Programmable Gate Array) in image processing applications is justified by its flexible reconfigurability, its inherent parallel nature and the availability of a large amount of internal memories. Lately, the Stochastic Computing (SC) paradigm has been found to be significantly advantageous in certain application domains including image processing because of its lower hardware complexity and power consumption. However, its viability is deemed to be limited due to its serial bitstream processing and excessive run-time requirement for convergence. To address these issues, a novel approach is proposed in this work where an energy-efficient implementation of SC is accomplished by introducing fast-converging Quasi-Stochastic Number Generators (QSNGs) and parallel stochastic bitstream processing, which are well suited to leverage FPGA’s reconfigurability and abundant internal memory resources. The proposed approach has been tested on the Virtex-4 FPGA, and results have been compared with the serial and parallel implementations of conventional stochastic computation using the well-known SC edge detection and multiplication circuits. Results prove that by using this approach, execution time, as well as the power consumption are decreased by a factor of 3.5 and 4.5 for the edge detection circuit and multiplication circuit, respectively. View Full-Text
Keywords: stochastic computing; FPGA; edge detection; quasi-stochastic number generator; reconfigurability stochastic computing; FPGA; edge detection; quasi-stochastic number generator; reconfigurability
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Seva, R.; Metku, P.; Choi, M. Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing. J. Low Power Electron. Appl. 2017, 7, 29.

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