Paul Franzon's website

Applying Machine Learning to Electronics Design Automation

Rapid Modeling of High Speed IO

Predicting 3D temperature map

BOOK CHAPTERS
  1. Yi Wang, P. Franzon, “Solving the inverse problem through optimization – Applications to Analog/RF IC Design,” to appear in “Surrogate Modeling for High-Frequency Design – Recent Advances”, S. Koziel (ed). 2021. (World Scientific)
  1. Eric Wyers, Tim Kelley, and Paul Franzon, “Optimization for Self-Calibrating Circuits,” in Semiconductor Devices in Harsh Conditions, (CRC), 2015.
  1. Ting Zhu, Mustafa Berke Yelten, Michael B. Steer, and Paul D. Franzon, “Model-based Variation Aware Integrated Circuit Design”, (Springer)
JOURNAL PUBLICATIONS
  1. L. Francisco, W. R. Davis and P. Franzon, “A Deep Transfer Learning Design Rule Checker With Synthetic Training,” in IEEE Design & Test, vol. 40, no. 1, pp. 77-84, Feb. 2023. doi: 10.1109/MDAT.2022.3162786
  2. P. Kashyap, F. Aydin, S. Potluri, P. D. Franzon and A. Aysu, “2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 6, pp. 1217-1229, June 2021.
  1. P. Kashyap, F. Aydin, S. Potluri, P. Franzon and A. Aysu, “2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2D Deep Learning,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020.
  1. B. Li, B. Jiao, C. Chou, R. Mayder and P. Franzon, “Self-Evolution Cascade Deep Learning Model for High-Speed Receiver Adaptation,” in IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 10, no. 6, pp. 1043-1053, June 2020.
  1. Yi Wang, P. Franzon, D. Smart, B. Swahn, “Multi-fidelity surrogate-based optimization for electromagnetic simulation acceleration”, ACM Trans. Design Automation of Electronic Systems, Aug. 2020, No. 45
  1. S. W. Park, L. B. Baker and P. D. Franzon, “Appliance Identification Algorithm for a Non-Intrusive Home Energy Monitor Using Cogent Confabulation,” in IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 714-721, Jan. 2019.
  1. E. J. Wyers, M. A. Morton, T. C. L. G. Sollner, C. T. Kelley and P. D. Franzon, “A Generally Applicable Calibration Algorithm for Digitally Reconfigurable Self-Healing RFICs,” in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 24, no. 3, pp. 1151-1164, March 2016.
  1. Wyers, E.J.; Steer, M.B.; Kelley, C.T.; Franzon, P.D., “A Bounded and Discretized Nelder-Mead Algorithm Suitable for RFIC Calibration,” Circuits and Systems I: Regular Papers, IEEE Transactions on , vol.60, no.7, pp.1787,1799, July 2013
  1. T. Zhu, M. Steer, P. Franzon, “Surrogate model-based self-calibrated design for process and temperature compensation in Analog/RF circuits,” in IEEE Design and Test, Vol. PP, No. 99, 2013.
  1. M.B. Yelten, T. Zhu, S. Koziel, P.D. Franzon, M.B. Steer, “Demystifying Surrogate Modeling for Circuits and Systems,” in IEEE Circuits and Systems Magazine, VOl. 12, No. 1, 2012, pp. 45-63.
  1. T. Zhu, M.B. Steer, and P.D. Franzon, “Accurate and Scalable IO Buffer Macromodel Based on Surrogate Modeling,”  in IEEE Transactions CPMT, VOl. 1, Issue 8, 2011, pp. 1240-1249.
  1. M.B. Yelten, P.D. Franzon and M.B. Steer, “Surrogate Model-Based Analysis of Analog Circuits – Part I. Variability Analysis,” in IEEE Trans. Device and Materials Reliability, Vol. PP, Issue 99, 2011.
  1. M.B. Yelten, P.D. Franzon and M.B. Steer, “Surrogate Model-Based Analysis of Analog Circuits – Part II. Reliability Analysis,” in IEEE Trans. Device and Materials Reliability, Vol. PP, Issue 99, 2011.
  1. A. Varma, M.B. Steer, and P.D. Franzon, “Improving Behavioral IO Buffer Modeling Based on IBIS,” in IEEE Trans. Adv. Pack., VOl. 31, No. 4, Nov. 2008, pp. 711-721.
PEER REFEREED CONFERENCE PUBLICATIONS
  1. P. Kashyap, C. Cheng, Y. Choi and P. Franzon, “Generative Multi-Physics Models for System Power and Thermal Analysis Using Conditional Generative Adversarial Networks,” 2023 IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), Milpitas, CA, USA, 2023, pp. 1-3, doi: 10.1109/EPEPS58208.2023.10314864.
  1. T. -H. Pan, P. D. Franzon, V. Srinivas, M. Nagarajan and D. Popovic, “System Aware Floorplanning for Chip-Package Co-design,” 2023 IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), Milpitas, CA, USA, 2023, pp. 1-3, doi: 10.1109/EPEPS58208.2023.10314897.
  1. F. Amin, S. Chatterjee and P. D. Franzon, “DepthGraphNet: Circuit Graph Isomorphism Detection via Siamese-Graph Neural Networks,” 2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD), Snowbird, UT, USA, 2023, pp. 1-6, doi: 10.1109/MLCAD58807.2023.10299839.
  1. P. Kashyap, P. P. Ravichandiran, D. Baron, C. -W. Wong, T. Wu and P. D. Franzon, “Generative Adversarial Network Based Adaptive Transmitter Modeling,” 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), Orlando, FL, USA, 2023, pp. 2183-2187, doi: 10.1109/ECTC51909.2023.00376..
  1. P. Kashyap et al., “Thermal Estimation for 3D-ICs Through Generative Networks,” 2023 IEEE International 3D Systems Integration Conference (3DIC), Cork, Ireland, 2023, pp. 1-4, doi: 10.1109/3DIC57175.2023.10154977.
  1. Y. Wen, J. Dean, B. A. Floyd and P. D. Franzon, “High Dimensional Optimization for Electronic Design,” 2022 ACM/IEEE 4th Workshop on Machine Learning for CAD (MLCAD), UT, USA, 2022, pp. 153-157.
    doi: 10.1109/MLCAD55463.2022.9900104
  1. P. Kashyap et al., “RxGAN: Modeling High-Speed Receiver through Generative Adversarial Networks,” 2022 ACM/IEEE 4th Workshop on Machine Learning for CAD (MLCAD), UT, USA, 2022, pp. 167-172.
    doi: 10.1109/MLCAD55463.2022.9900088
  1. P. Kashyap et al., “Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks,” 2022 IEEE 72nd Electronic Components and Technology Conference (ECTC), San Diego, CA, USA, 2022, pp. 1958-1963.
    doi: 10.1109/ECTC51906.2022.00307
  1. A. Gajjar, P. Kashyap, A. Aysu, P. Franzon, S. Dey and C. Cheng, “FAXID: FPGA-Accelerated XGBoost Inference for Data Centers using HLS,” 2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), New York City, NY, USA, 2022, pp. 1-9.
    doi: 10.1109/FCCM53951.2022.9786085
  1. W. R. Davis, P. Franzon, L. Francisco, B. Huggins and R. Jain, “Fast and Accurate PPA Modeling with Transfer Learning,” 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Munich, Germany, 2021, pp. 1-8.
    doi: 10.1109/ICCAD51958.2021.9643533. Invited Paper.
  1. W. R. Davis, P. Franzon, L. Francisco, B. Huggins and R. Jain, “Fast and Accurate PPA Modeling with Transfer Learning,” 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Munich, Germany, 2021, pp. 1-8.
  1. P. Kashyap, W. S. Pitts, D. Baron, C. -W. Wong, T. Wu and P. D. Franzon, “High Speed Receiver Modeling Using Generative Adversarial Networks,” 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), Austin, TX, USA, 2021, pp. 1-3.
  1. L. Francisco, P. Franzon and W. R. Davis, “Fast and Accurate PPA Modeling with Transfer Learning,” 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD), Raleigh, NC, USA, 2021, pp. 1-6.
  1. F. Regazzoni et al., “Machine Learning and Hardware security: Challenges and Opportunities -Invited Talk-,” 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), San Diego, CA, USA, 2020, pp. 1-6.
  1. B. Li, B. Jiao, C. -H. Chou, R. Mayder and P. Franzon, “CTLE Adaptation Using Deep Learning in High- speed SerDes Link,” 2020 IEEE 70th Electronic Components and Technology Conference (ECTC), Orlando, FL, USA, 2020, pp. 952-955.
  1. I. Turteltaub, G. Li, M. Ibrahim, P. Franzon, “Application of quantum machine learning to VLSI placement,” in Proc. ACM/IEEE MLCAD, Nov. 2020. Pp.61-66.
  1. L. Francisco, et.al, “Design rule checking with a CNN based feature extreactor,” in Proc. ACM/IEEE MLCAD 2020,, Nov. 2020, p. 61-66.
  1. Bowen Li, Paul Franzon, Naomi Price, Brandon Jiao, Geoff Zhang, Chi-Hsun Chao, “Self Evolution Cascade Deep Learning for SerDes Adaptive Equalization,” in Proc. DesignCon 2020, San Jose Ca.  Won best paper award.
  1. B. Huggins, W. R. Davis and P. Franzon, “Estimating Pareto Optimum Fronts to Determine Knob Settings in Electronic Design Automation Tools,” 20th International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA, 2019, pp. 304-310.
  1. Y. Wang and P. D. Franzon, “RFIC IP Redesign and Reuse Through Surrogate Based Machine Learning Method,” 2018 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), Reykjavik, 2018, pp. 1-4.
  1. E. J. Wyers, W. Qi and P. D. Franzon, “A robust calibration and supervised machine learning reliability framework for digitally-assisted self-healing RFICs,” 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, 2017, pp. 1138-1141.
  1. B. Li and P. D. Franzon, “Machine learning in physical design,” 2016 IEEE 25th Conference on Electrical Performance Of Electronic Packaging And Systems (EPEPS), San Diego, CA, 2016, pp. 147-150.