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Paper ID:IJSREM67675

Comparative Performance Evaluation of Different 8-Bit Approximate Multipliers for Computer Vision Applications

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67675

Volume:

10 |

10 |

Oct 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67675

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KS

Kuldeep Singh

Department of Electrical and Electronics Engineering,

Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

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MJ

Manisha Jangra

Department of Electrical and Electronics Engineering,

Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

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RB

Ritu Boora

Department of Electrical and Electronics Engineering,

Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

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VK

Vinod Kumar

Department of Electrical and Electronics Engineering,

Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

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AK

Ajay Kumar

Department of Electrical and Electronics Engineering,

Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

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Abstract

Approximate multiplication is attractive for error-resilient computer-vision datapaths because multiplication dominates many convolution, filtering, gradient, and feature-extraction kernels. This work experimentally compares four unsigned 8-bit multiplier architectures—an exact reference, an ordinary column-truncated multiplier, a fixed-bias truncated multiplier, and a three-boundary-column compensated (BWC-3) multiplier—under a common registered FPGA benchmark. All architectures are evaluated on a Xilinx Zynq-7000 XC7Z020-CLG484-1 device using a 14-point post-route timing sweep from 50 to 200 MHz. The reported implementation data show markedly different timing–area–power trade-offs. The exact design maintains non-negative slack through 180.995 MHz, whereas ordinary truncation and fixed bias close timing through 200 MHz; BWC-3 closes only through 133 MHz in the measured sweep because its adaptive correction logic lengthens the critical path. At 200 MHz, fixed bias requires 68 LUTs and 0.021 W dynamic power compared with 84 LUTs and 0.029 W for the exact reference. The results demonstrate that approximation quality and FPGA implementation efficiency must be considered jointly when selecting multiplier cores for vision-oriented hardware.

KEYWORDS

Approximate computing truncated multiplier fixed-bias compensation BWC-3 FPGA Zynq-7000 timing closure computer vision power-delay product.

Cite This Article

Kuldeep Singh, Manisha Jangra, Ritu Boora, Vinod Kumar, Ajay Kumar, "Comparative Performance Evaluation of Different 8-Bit Approximate Multipliers for Computer Vision Applications," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-9, Oct 2026. https://doi.org/10.55041/IJSREM67675

Full Article/Conclusion

A unique14-point FPGA timing sweep was planned to compare the exact, ordinary truncated, fixed-bias, and BWC-3 8-bit multipliers using integrated wrapper. The measurements reveal a clear trade-off among timing robustness, and implementation hardware cost. The ordinary truncation multiplier is the smallest design and meets timing at 200 MHz, fixed bias also preserving 200-MHz timing closure and retaining lower LUT, dynamic-power, PDP, and ADP values compare with the exact reference. The BWC-3 architecture have the highest arithmetic accuracy, but its additional compensation logic limits measured timing closure to 133 MHz and increases LUT/ADP cost for hardware. For high-throughput vision architecture datapaths, the observed data thus identified fixed bias as a balanced implementation point, while BWC-3 is better for accuracy demanding operations.

References

 [1] N. Petra, D. De Caro, V. Garofalo, E. Napoli, and A. G. M. Strollo, “Truncated binary multipliers with variable correction and minimum mean square error,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 57, no. 6, pp. 1312–1325, Jun. 2010, doi: 10.1109/TCSI.2009.2033536. [2] V. Garofalo, N. Petra, and E. Napoli, “Analytical calculation of the maximum error for a family of truncated multipliers providing minimum mean square error,” IEEE Trans. Comput., vol. 60, no. 9, pp. 1366–1371, Sep. 2011, doi: 10.1109/TC.2010.236. [3] N. Petra, D. De Caro, V. Garofalo, E. Napoli, and A. G. M. Strollo, “Design of fixed-width multipliers with linear compensation function,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 58, no. 5, pp. 947–960, May 2011, doi: 10.1109/TCSI.2010.2090572. [4] G. Zervakis, K. Tsoumanis, S. Xydis, D. Soudris, and K. Pekmestzi, “Design-efficient approximate multiplication circuits through partial product perforation,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 24, no. 10, pp. 3105–3117, Oct. 2016, doi: 10.1109/TVLSI.2016.2535398. [5] S. Vahdat, M. Kamal, A. Afzali-Kusha, and M. Pedram, “TOSAM: An energy-efficient truncation- and rounding-based scalable approximate multiplier,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 27, no. 5, pp. 1161–1173, May 2019, doi: 10.1109/TVLSI.2018.2890712. [7] Y. Wu, C. Chen, W. Xiao, X. Wang, C. Wen, J. Han, X. Yin, W. Qian, and C. Zhuo, “A survey on approximate multiplier designs for energy efficiency: From algorithms to circuits,” ACM Trans. Design Autom. Electron. Syst., vol. 29, no. 1, Art. no. 23, pp. 1–37, Jan. 2024, doi: 10.1145/3610291. [8] L. Sayadi, S. Timarchi, and A. Sheikh-Akbari, “Two efficient approximate unsigned multipliers by developing new configuration for approximate 4:2 compressors,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 70, no. 4, pp. 1649–1659, Apr. 2023, doi: 10.1109/TCSI.2023.3242558. [9] S. Shakibhamedan, N. Amirafshar, A. S. Baroughi, H. S. Shahhoseini, and N. TaheriNejad, “ACE-CNN: Approximate carry disregard multipliers for energy-efficient CNN-based image classification,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 71, no. 5, pp. 2280–2293, May 2024, doi: 10.1109/TCSI.2024.3369230. [10] Z. Li, S. Zheng, J. Zhang, Y. Lu, J. Gao, J. Tao, and L. Wang, “Adaptable approximate multiplier design based on input distribution and polarity,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 30, no. 12, pp. 1813–1826, Dec. 2022, doi: 10.1109/TVLSI.2022.3197229.

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Publication Date

10/5/2026

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