Abstract

Approximate or inaccurate addition is found to be viable for practical applications which have an inherent error tolerance. Approximate addition is realized using an approximate adder, and many approximate adder designs have been put forward in the literature targeting an acceptable trade-off between quality of results and savings in design metrics compared to the accurate adder. Approximate adders can be classified into three categories as: (a) suitable for FPGA implementation, (b) suitable for ASIC type implementation, and (c) suitable for FPGA and ASIC type implementations. Among these, approximate adders, which are suitable for FPGA and ASIC type implementations are particularly interesting given their versatility and they are typically designed at the gate level. Depending on the way approximation is built into an approximate adder, approximate adders can be classified into two kinds as static approximate adders and dynamic approximate adders. This paper compares and analyzes static approximate adders which are suitable for both FPGA and ASIC type implementations. We consider many static approximate adders and evaluate their performance for a digital image processing application using standard figures of merit such as peak signal to noise ratio and structural similarity index metric. We provide the error metrics of approximate adders, and the design metrics of accurate and approximate adders corresponding to FPGA and ASIC type implementations. For the FPGA implementation, we considered a Xilinx Artix-7 FPGA, and for an ASIC type implementation, we considered a 32/28 nm CMOS standard digital cell library. While the inferences from this work could serve as a useful reference to determine an optimum static approximate adder for a practical application, in particular, we found approximate adders HOAANED, HERLOA and M-HERLOA to be preferable.

Highlights

  • Computation-intensive technologies such as artificial intelligence, machine learning, big data and analytics, data mining, cloud computing, Internet-of-Things, etc., often deal with a data deluge, which makes processing using accurate computing techniques expensive in terms of time and resources

  • HERLOA and M-HERLOA consistently result in almost the peak signal to noise ratio (PSNR) and structural similarity index metric (SSIM) calculated for the images reconstructed using different approximate same SSIM, which is greater than the SSIM of images reconstructed using other approximate adders are given in Tables 1 and 2, respectively

  • Digital image processing was considered as an example application and the image processing results were shown

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Summary

Introduction

Computation-intensive technologies such as artificial intelligence, machine learning, big data and analytics, data mining, cloud computing, Internet-of-Things, etc., often deal with a data deluge, which makes processing using accurate computing techniques expensive in terms of time and resources In such cases, it would be more feasible and economical if computing is performed such that the results are sufficiently correct, which is called approximate, inaccurate or imprecise computing. Approximation is fixed in an SAA that may produce an accurate sum or an approximate sum corresponding to a specified accuracy in a single clock cycle and guarantees assured savings in design metrics compared to the accurate adder.

Gate-Level Static Approximate Adders
Digital Image Processing Using Accurate and Approximate Adders
Error parameters ofof size bits comprising a
Accurate and Approximate Adders—Implementation Results
Design metrics accurate and approximate adders synthesized using
Findings
Conclusions
Full Text
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