Machine Learning Based Power Estimation for CMOS VLSI Circuits

References

• Amuru, M. S. A., and Z. Abbas 2020. An efficient gradient boosting approach for PVT aware estimation of leakage power and propagation delay in CMOS/FinFET digital cells. IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, 1–5. doi: https://doi.org/10.1109/ISCAS45731.2020.9180600.
   Google Scholar
• Bhanja, S., and N. Ranganathan. 2003. Switching activity estimation of VLSI circuits using Bayesian networks. IEEETransactions on Very Large Scale Integration Systems 11 (4):558–67. doi:https://doi.org/10.1109/TVLSI.2003.816144.
   Web of Science ®Google Scholar
• Burch, R., F. N. Najm, P. Yang, and T. N. Trick. 1993. A Monte Carlo approach for power estimation. IEEE Transactions on Very Large Scale Integration Systems 2 (1):63–71. doi:https://doi.org/10.1109/92.219908.
   Google Scholar
• Buyuks, M. K., and F. N. Najm. 2006. Early power estimation for VLSI circuits. IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems 24 (7):1076–88. doi:https://doi.org/10.1109/TCAD.2005.850904.
   Web of Science ®Google Scholar
• Chen, B., P. Chen, A. N. Belkacem, L. Lu, R. Xu, W. Tan, P. Li, Q. Gao, D. Shin, C. Wang, et al. 2020. Neural activities classification of left and right finger gestures during motor execution and motor imagery. Brain-Computer Interfaces 1–11. doi:https://doi.org/10.1080/2326263X.2020.1782124.
   Google Scholar
• Das, A. K., S. Dalai, and B. Chatterjee 2020. Cross stockwell transform aided random forest based surface condition identification of metal oxide surge arrester employning leakage current signal. 2020 IEEE Region 10 Symposium (TENSYMP), 1775–78. doi: https://doi.org/10.1109/TENSYMP50017.2020.9230802.
   Google Scholar
• De Santos, H., and M. Á. Sanz-Bobi. 2020. A cumulative pollution index for the estimation of the leakage current on insulator strings. IEEE Transactions on Power Delivery 35 (5):2438–46. doi:https://doi.org/10.1109/TPWRD.2020.2968556.
   Web of Science ®Google Scholar
• Govindaraj, V., and J. Ramesh. 2018. Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits. International Journal of Electronics, Taylor and Francis 105 (3):398–411.
   Web of Science ®Google Scholar
• Harris, C. J. 1994. Advances in intelligent control. Boca Raton, FL: CRC Press. ISBN 9780748400669.
   Google Scholar
• Hou, L., L. Zheng, and W. Wu 2006. Neural network based VLSI power estimation. 2006 8th International Conference on Solid-State and Integrated Circuit Technology Proceedings. doi:https://doi.org/10.1109/icsict.2006.306506
   Google Scholar
• Kirei, S. C. F., and M. D. Topa 2019. Power and area estimation of discrete filters in CMOS integrated circuits. Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, Poland, 67–70, doi: https://doi.org/10.23919/SPA.2019.8936762.
   Google Scholar
• Kozhaya, J. N., and F. N. Najm. 2001. Power estimation for large sequential circuits. IEEE Transactions on Very Large Scale Integration Systems 9 (2):400–07. doi:https://doi.org/10.1109/ICCAD.1997.643581.
   Google Scholar
• Liang, H. G., and T. Zheng. 2019. Real-time impedance estimation for power line communication. IEEE Access 7:88107–15. doi:https://doi.org/10.1109/ACCESS.2019.2925464.
   Web of Science ®Google Scholar
• Murugavel, A. K., N. Ranganathan, R. Chandramouli, and S. Chavali. 2002. Least-square estimation of average power in digital CMOS circuits. IEEE Transactions on Very Large Scale Integration Systems 10 (1):55–58. doi:https://doi.org/10.1109/92.988730.
   Web of Science ®Google Scholar
• Nasser, Y., J. Lorandel, J.-C. Prevotet, and M. Hélard. 2021. RTL to transistorlevel power modelling and estimation techniques for FPGA and ASIC: A survey. IEEE Transac-tions on Computer-Aided Design of Integrated Circuits and Systems 40 (3):479–93. doi:https://doi.org/10.1109/TCAD.2020.3003276.
   Web of Science ®Google Scholar
• Omnia S. Fadl, Mohamed F. Abu-Elyazeed, Mohamed B. Abdelhalim, Hassanein H. Amer, Ahmed H. Madian(2016). Accurate dynamic power estimation for CMOS combinational logic circuits with real gate delay model. Journal of Advanced Research 7 (1) 89-94. ISSN 2090-1232. https://doi.org/https://doi.org/10.1016/j.jare.2015.02.006
   Google Scholar
• Pandey, S., J. Schumacher, and K. R. Sreenivasan. 2020. A perspective on machine learning in turbulent flows. Journal of Turbulence 21 (9–10):567–84. doi:https://doi.org/10.1080/14685248.2020.1757685.
   Web of Science ®Google Scholar
• Peng, H., A. R. Lima, and A. Teakles. 2017. Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Quality, Atmosphere, & Health 10 (2):195–211, March. doi:https://doi.org/10.1007/s11869-016-0414-3.
   Web of Science ®Google Scholar
• Pilarski, M. R. D., T. Degris, J. P. Carey, and R. S. Sutton 2012. Dynamic switching and real-time machine learning for improved human control of assistive biomedical robots. 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy, June, 296–302.
   Google Scholar
• Pradhan, K., and P. Chawla. 2020. Medical Internet of things using machine learning algorithms for lung cancer detection. Journal of Management Analytics 7 (4):591–623. doi:https://doi.org/10.1080/23270012.2020.1811789.
   Web of Science ®Google Scholar
• Ramanathan, P., B. Surendiran, and P. T. Vanathi. 2013. Power estimation of benchmark circuits using artificial neural networks. Pensee Journal 75 (9):427–33.
   Google Scholar
• Saxena, V., F. N. Najm, and I. N. Hajj 1997. Monte-Carlo approach for power estimation in sequential circuits. European Design and Test conference, (ED&TC 97), March 17–20, 416–20. doi:https://doi.org/10.1109/EDTC.1997.582393.
   Google Scholar
• Seyedzadeh, F. P. R., I Glesk, and M. Roper. 2018. Machine learning for estimation of building energy consumption and performance: A review. Visualization in Engineering 6:5. doi:https://doi.org/10.1186/s40327-018-0064-7.
   Google Scholar
• Shaw Halwai, K. K. 2020. Estimation of power and delay of CMOS phase detector and phase-frequency detector using nano dimensional MOS transistor. IEEE VLSI Device Circuit and System (VLSI DCS), Kolkata, India, 294–98. doi: https://doi.org/10.1109/VLSIDCS47293.2020.9179921.
   Google Scholar
• Srivastava, S. S., and A. P. Singh 2018.Estimation of air pollution in Delhi using machine learning techniques. International Conference on Computing, Power and Communication Technologies (GUCON), 304–09. doi: https://doi.org/10.1109/GUCON.2018.8675022.
   Google Scholar
• Yehya Nasser, C. S., J.-C. Pr´evotet, T. Fanni, F. Palumbo, M. H´elard, and L. Raffo. 2020. NeuPow: A CAD methodology for high level power estimation based on machine learning. ACM Transactions on Design Automation of Electronic Systems 25 (5):1–29. doi: https://doi.org/10.1145/3388141.
   Web of Science ®Google Scholar