Abstract
Approximate computing allows substantial power saving at the cost of curtailed accuracy. The inaccurate outputs are tolerated by applications such as image and multimedia processing, data mining, etc. due to their inherent error tolerance. Multipliers are the most prominent circuits which are required for deploying these applications on digital processors. In this paper, a power-efficient unsigned multiplier is designed that uses a novel approximate 4–2 compressor. Simulation results demonstrate that the proposed multipliers have 11.07\(\%\) reduction in energy compared to exact multiplier and provide a high PSNR in the range of 20.47 to 35.23 dB for image multiplication application.
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References
Han, J., & Orshansky, M. (2013). Approximate computing: An emerging paradigm for energy-efficient design. In Proceedings of the 18th IEEE European test symposium (ETS), 2013 (pp. 1–6)
Wang, G., & Shield, J. (2005). The efficient implementation of an array multiplier. In 2005 IEEE international conference on electro information technology, 2005 (p. 5).
Venkatachalam, S., Adams, E., Lee, H. J., & Ko, S. (2019). Design and analysis of area and power efficient approximate booth multipliers. IEEE Transactions on Computers, 68(11), 1697–1703.
Liu, W., Qian, L., Wang, C., Jiang, H., Han, J., & Lombardi, F. (2017). Design of approximate radix-4 booth multipliers for error-tolerant computing. IEEE Transactions on Computers, 66(8), 1435–1441.
Leon, V., Zervakis, G., Xydis, S., Soudris, D., & Pekmestzi, K. (2018). Walking through the energy-error pareto frontier of approximate multipliers. IEEE Micro, 38(4), 40–49.
Leon, V., Asimakopoulos, K., Xydis, S., Soudris, D., & Pekmestzi, K. (2019). Cooperative arithmetic-aware approximation techniques for energy-efficient multipliers. In Proceedings of the 56th annual design automation conference, 2019 (pp. 1–6).
Momeni, A., Han, J., Montuschi, P., & Lombardi, F. (2015). Design and analysis of approximate compressors for multiplication. IEEE Transactions on Computers, 64(4), 984–994.
Ha, M., & Lee, S. (2018). Multipliers with approximate 4–2 compressors and error recovery modules. IEEE Embedded System Letters, 10(1), 6–9.
Lin, C.-H., & Lin, I.-C. (2013). High accuracy approximate multiplier with error correction. In Proceedings of the 31st ICCD conference (pp. 33–38).
Yang, Z., Han, J., & Lombardi, F. (2015). Approximate compressors for error resilient multiplier design. In Proceedings of the IEEE international symposium defect fault tolerance VLSI nanotechnology system, Oct. 2015 (pp. 183–186).
Sabetzade, F., Moaiyeri, M. H., & Ahmadinejad, M. (2019). A majority based imprecise multiplier for ultra-efficient approximate image multiplication. IEEE Transactions on Circuits and Systems I: Regular Papers, 66(11), 4200–4208.
Venkatachalam, S., & Ko, S.-B. (2017). Design of power and area effcient approximate multipliers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(5), 1782–1786.
Akbari, O., Kamal, M., Afzali-Kusha, A., & Pedram, M. (2017). Dual-quality 4:2 compressors for utilizing in dynamic accuracy configurable multi pliers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(4), 1352–1361.
Ahmadinejad, M., Moaiyeri, M. H., & Sabetzade, F. (2019). Energy and area efficient imprecise compressors for approximate multiplication at nanoscale. AEU - International Journal of Electronics and Communications, 110, Art. no. 152859.
Strollo, A. G. M., Napoli, E., De Caro, D., Petra, N., & Meo, G. D. (2020). Comparison and extension of approximate 4–2 compressors for low-power approximate multipliers. IEEE Transactions on Circuits and Systems I: Regular Papers, 67(9), 3021–3034.
Narayanamoorthy, S., Moghaddam, H. A., Liu, Z., Park, T., & Kim, N. S. (2015). Energy-efficient approximate multiplication for digital signal process ing and classification applications. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 23(6), 1180–1184.
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Aizaz, Z., Khare, K., Tirmizi, A. (2023). Approximate Computing-Based Unsigned Multipliers for Image Processing Applications. In: Darji, A.D., Joshi, D., Joshi, A., Sheriff, R. (eds) Advances in VLSI and Embedded Systems. Lecture Notes in Electrical Engineering, vol 962. Springer, Singapore. https://doi.org/10.1007/978-981-19-6780-1_7
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DOI: https://doi.org/10.1007/978-981-19-6780-1_7
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