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            "value": "Approximate computing improves performance in error-resilient applications like image andvideo processing. Multipliers are part of its computing unit, which frequently requires a large numberofresources.Thisstudycomparesanapproximation(8;2)compressortootherknownmodelsintermsof quality, power consumption, delay, and circuit area. The proposed approximation compressor isimplementedin8×8and16×16multipliers.Todemonstratethequalityofthesuggestedcompressor,an8×8approximationmultiplierwas utilizedtomultiplytwoimagesinMATLABtools.QualitativemeasuressuchasSSIMandPSNRwereexaminedandacceptableresultswereobtained. Thesuggested8×8multiplier circuitproducesan acceptableerrorrate,asindicatedbytheMEDandNEDaccuracycriteria. Finally, we used a Synopsys Design Compiler to synthesize the proposed approximationcompressorandmultiplierdesigns.Thesuggested16×16multiplierimproveslatency,area,andpowerdelayproductsby5%,17%,and8%,respectively,comparedtosimilarcurrentapproximatemultipliers.",
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                "unstructured": "N. Wu et al., “Application of Approximate Multipliers in Biomedical Signal Processing,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 112-120, Aug. 2020"
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                "unstructured": "P. Wang et al., “Energy-Efficient Multiplier Architectures for Cryptographic Circuits Using Approximate Computing,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 26, no. 6, pp. 1123-1135, June 2018"
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                "unstructured": "R. Liu et al., “Approximate Computing Techniques for Real-Time Signal Processing Multipliers,” IEEE Transactions on Signal Processing, vol. 64, no. 7, pp. 1765-1778, April 2016"
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                "key": "ref13",
                "unstructured": "S. Li et al., “Approximate Computing Techniques for Neural Network Accelerators,” IEEE K. Babu Rao, Z. Naga Sivareddy, P. Aravind, T. Rakesh Babu: Performance Evaluation of Approximate (8; 2) Compressor for Multipliers in Error-Resilient Image Processing Applications Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1123-1135, May 2018"
            },
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                "key": "ref14",
                "unstructured": "T. Zhang et al., “Optimization of Approximate Multipliers for Low-Power Digital Signal Processing Applications,” Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 567-575, June 2019"
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                "key": "ref15",
                "unstructured": "U. Wang et al., “Design of Reconfigurable Computing Architectures Using Approximate Computing Techniques,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 9, pp. 2015-2027, Sept. 2020"
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                "key": "ref16",
                "unstructured": "V. Zhang et al., “Application of Approximate Multipliers in IoT Edge Devices,” Proceedings of the IEEE Conference on Computer Communications, pp. 112-120, May 2017"
            },
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                "key": "ref17",
                "unstructured": "W. Chen et al., “Optimization of Approximate Multipliers for Energy-Constrained Embedded Systems,” Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, pp. 340-352, Aug. 2021"
            },
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                "key": "ref18",
                "unstructured": "X. Zhang et al., “Approximate Computing Techniques for Hardware Security Primitives,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 6, pp. 1765-1778, June 2019"
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