Open Access

ARTICLE

Optimized Image Multiplication with Approximate Counter Based Compressor

by M. Maria Dominic Savio^1,*, T. Deepa¹, N. Bharathiraja², Anudeep Bonasu³

1 SRMIST, Chennai, 603203, India
2 Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 601206, India
3 Intel Corportation, California, 93657, USA

* Corresponding Author: M. Maria Dominic Savio. Email:

Computers, Materials & Continua 2022, 72(2), 3815-3834. https://doi.org/10.32604/cmc.2022.025924

Received 09 December 2021; Accepted 16 February 2022; Issue published 29 March 2022

Abstract

The processor is greatly hampered by the large dataset of picture or multimedia data. The logic of approximation hardware is moving in the direction of multimedia processing with a given amount of acceptable mistake. This study proposes various higher-order approximate counter-based compressor (CBC) using input shuffled 6:3 CBC. In the Wallace multiplier using a CBC is a significant factor in partial product reduction. So the design of 10-4, 11-4, 12-4, 13-4 and 14-4 CBC are proposed in this paper using an input shuffled 6:3 compressor to attain two stage multiplications. The input shuffling aims to reduce the output combination of the 6:3 compressor from 64 to 27. Design of 15-4, 10-4, 9-4, and 7-3 CBCs are performed using the proposed 6:3 compressor and the results obtained are compared with the existing models. These existing models are constructed using multiplexers and 5-3 CBC. When compared to input shuffled 5-3 the proposed 6:3 compressor shows better results in terms of area, power and delay. An approximation is performed on the 6:3 compressor to further reduce the computational energy of the system which is optimal for multimedia applications. The major contribution of this work is the development of two stage multiplier using various proposed CBC. All designs of the approximate compressor (AC) and true compressor (TC) are analysed with 8 x 8 and 16 x 16 image multiplication. The proposed multipliers also provide adequate levels of accuracy, according to the MATLAB simulations, in addition to greater hardware efficiency. As the result approximate circuits over image processing shows the stunning performance in many deep learning network in the current research which is only oriented to multimedia.

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Keywords

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