Design and Implementation of Reconfigurable Approximation Technique for Arithmetic Unit

Authors(2) :-I. S. Priya, B. Venkatesh

The research community in the last few years from the field of approximate computing has received significant attention, particularly in the context of different signal processing. Image and video compression algorithms such as JPEG, MPEG and so on, which can be exploited to realize highly power efficient implementations of these algorithms. However, existing approximate architectures typically fix the level of hardware approximations statically and are not adaptive to input data. This project addresses this issue by proposing a reconfigurable approximate for MPEG encoders that optimizes power consumption with the aim of maintaining a particular peak signal-to noise ratio threshold for any video. We propose two heuristics for automatically tuning the approximation degree of the RABs in these two modules during runtime based on the Characteristics of each individual video. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.3.

Authors and Affiliations

I. S. Priya
M. Tech Student, Head Of The Department, ECE Shree Institute of Technical Education, Tirupathi, India
B. Venkatesh
Assistant Professor & Head Of The Department, ECE Shree Institute of Technical Education, Tirupathi, India

Approximate circuits, approximate computing, low power design, quality configurable.

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 36-39
Manuscript Number : CSEIT1836154
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

I. S. Priya, B. Venkatesh, "Design and Implementation of Reconfigurable Approximation Technique for Arithmetic Unit ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.36-39, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT1836154

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