Improved approximate multiplier architecture for image processing and neural network applications

Microprocessors and Microsystems - Tập 101 - Trang 104909 - 2023
Pramod Alamuri1, U. Anil Kumar2, Vallepu Vannuru1, Syed Ershad Ahmed1
1Department of Electrical Engineering, BITS, Pilani, Hyderabad Campus, Hyderabad, India
2ECE Department, B V Raju Institute of Technology, Vishnupur, Narsapur, Telangana, India

Tài liệu tham khảo

Weiqiang, 2020, A retrospective and prospective view of approximate computing [point of view], Proc. IEEE, 108, 394, 10.1109/JPROC.2020.2975695 Liu, 2019, Design and analysis of majority logic-based approximate adders and multipliers, IEEE Trans. Emerg. Top. Comput., 9, 1609, 10.1109/TETC.2019.2929100 Kumar, 2022, Low-power compressor-based approximate multipliers with error correcting module, IEEE Embedded Syst. Lett., 14, 59, 10.1109/LES.2021.3113005 Zhang, 2022, Design of majority logic-based approximate booth multipliers for error-tolerant applications, IEEE Trans. Nanotechnol., 21, 81, 10.1109/TNANO.2022.3145362 Vahdat, 2019, TOSAM: An energy-efficient truncation-and rounding-based scalable approximate multiplier, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 27, 1161, 10.1109/TVLSI.2018.2890712 Ansari, 2019, Improving the accuracy and hardware efficiency of neural networks using approximate multipliers, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 28, 317, 10.1109/TVLSI.2019.2940943 Ansari, 2018, Low-power approximate multipliers using encoded partial products and approximate compressors, IEEE J. Emerg. Sel. Top. Circuits Syst., 8, 404, 10.1109/JETCAS.2018.2832204 Waris, 2020, Hybrid partial product-based high-performance approximate recursive multipliers, IEEE tetc-cs Nambi, 2020, DeBAM: Decoder based approximate multiplier for low power applications, IEEE Embedded Syst. Lett. Waris, 2021, AxRMs: Approximate recursive multipliers using high-performance building blocks, IEEE TETC Zacharelos, 2022, Approximate recursive multipliers using low power building blocks, IEEE Trans. Emerg. Top. Comput., 10, 1315, 10.1109/TETC.2022.3186240 Strollo, 2022, Approximate multipliers using static segmentation: Error analysis and improvements, IEEE Trans. Circuits Syst. I: Regul. Pap., 69, 2449, 10.1109/TCSI.2022.3152921 Yang, 2015, Approximate compressors for error resilient multiplier design, 183 Ha, 2017, Multipliers with approximate 4-2 compressors and error recovery modules, IEEE ESL, 10, 6 Venkatachalam, 2017, Design of power and area efficient approximate multipliers, IEEE TVLSI Syst., 25, 1782, 10.1109/TVLSI.2016.2643639 Yi, 2019, Design of an energy-efficient approximate compressor for error-resilient multiplications, 1 Strollo, 2020, Comparison and extension of approximate 4-2 compressors for low-power approximate multipliers, IEEE TCAS I: Regular Pap., 67, 3021 Sabetzadeh, 2023, An ultra-efficient approximate multiplier with error compensation for error-resilient applications, IEEE Trans. Circuits Syst. II: Express Briefs, 70, 776 LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 Tasoulas, 2020, Weight-oriented approximation for energy-efficient neural network inference accelerators, IEEE TCAS I Regular Pap., 67, 4670