# Center Publications

B. Li and P. D. Franzon, "Machine learning in physical design," *2016 IEEE 25th Conference on Electrical Performance Of Electronic Packaging And Systems (EPEPS)*, 2016, pp. 147-150, doi: 10.1109/EPEPS.2016.7835438.

S. J. Park, B. Bae, J. Kim and M. Swaminathan, "Application of Machine Learning for Optimization of 3-D Integrated Circuits and Systems," in *IEEE Transactions on Very Large Scale Integration (VLSI) Systems*, vol. 25, no. 6, pp. 1856-1865, June 2017, doi: 10.1109/TVLSI.2017.2656843.

H. M. Torun and M. Swaminathan, "Black-box optimization of 3D integrated systems using machine learning," *2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)*, 2017, pp. 1-3, doi: 10.1109/EPEPS.2017.8329698.

H. Yu, M. Swaminathan, C. Ji and D. White, "A method for creating behavioral models of oscillators using augmented neural networks," *2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)*, 2017, pp. 1-3, doi: 10.1109/EPEPS.2017.8329714.

Z. Chen, M. Raginsky and E. Rosenbaum, "Verilog-A compatible recurrent neural network model for transient circuit simulation," *2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)*, 2017, pp. 1-3, doi: 10.1109/EPEPS.2017.8329743.

T. Nguyen, J. E. Schutt-Aine and Y. Chen, "Volterra kernels extraction from frequency-domain data for weakly non-linear circuit time-domain simulation," *2017 IEEE Radio and Antenna Days of the Indian Ocean (RADIO)*, 2017, pp. 1-2, doi: 10.23919/RADIO.2017.8242244.

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)*, 2017, pp. 1138-1141, doi: 10.1109/MWSCAS.2017.8053129.

H. Yu, M. Swaminathan, C. Ji and D. White, "A Nonlinear Behavioral Modeling Approach for Voltage-controlled Oscillators Using Augmented Neural Networks," *2018 IEEE/MTT-S International Microwave Symposium - IMS*, 2018, pp. 551-554, doi: 10.1109/MWSYM.2018.8439324

Y. Xiu, S. Sagan, A. Battini, X. Ma, M. Raginsky and E. Rosenbaum, "Stochastic modeling of air electrostatic discharge parameters," *2018 IEEE International Reliability Physics Symposium (IRPS)*, 2018, pp. 2C.2-1-2C.2-10, doi: 10.1109/IRPS.2018.8353548.

X. Ma, M. Raginsky and A. C. Cangellaris, "A machine learning methodology for inferring network S-parameters in the presence of variability," *2018 IEEE 22nd Workshop on Signal and Power Integrity (SPI)*, 2018, pp. 1-4, doi: 10.1109/SaPIW.2018.8401643.

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)*, 2018, pp. 1-4, doi: 10.1109/NEMO.2018.8503446.

H. M. Torun, M. Swaminathan, A. Kavungal Davis and M. L. F. Bellaredj, "A Global Bayesian Optimization Algorithm and Its Application to Integrated System Design," in *IEEE Transactions on Very Large Scale Integration (VLSI) Systems*, vol. 26, no. 4, pp. 792-802, April 2018, doi: 10.1109/TVLSI.2017.2784783.

H. Torun & M. Swaminathan. Artificial Intelligence and Its Impact on System Design, *IEEE Electronic Components and Technology Conference (ECTC)*, San Diego, CA, May 29 – June 1, 2018.

T. Nguyen and J. E. Schutt-Aine, "A pseudo-supervised machine learning approach to broadband LTI macro-modeling," *2018 IEEE International Symposium on Electromagnetic Compatibility and 2018 IEEE Asia-Pacific Symposium on Electromagnetic Compatibility (EMC/APEMC)*, 2018, pp. 1018-1021, doi: 10.1109/ISEMC.2018.8393939.

J. Xiong, Z. Chen, Y. Xiu, Z. Mu, M. Raginsky and E. Rosenbaum, "Enhanced IC Modeling Methodology for System-level ESD Simulation," *2018 40th Electrical Overstress/Electrostatic Discharge Symposium (EOS/ESD)*, 2018, pp. 1-10, doi: 10.23919/EOS/ESD.2018.8509751.

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)*, 2018, pp. 1-4, doi: 10.1109/NEMO.2018.8503446.

H. M. Torun, J. A. Hejase, J. Tang, W. D. Beckert and M. Swaminathan, "Bayesian Active Learning for Uncertainty Quantification of High Speed Channel Signaling," *2018 IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)*, 2018, pp. 311-313, doi: 10.1109/EPEPS.2018.8534251. **INDUSTRY CO-AUTHOR**

H. Yu, H. Chalamalasetty and M. Swaminathan, "Behavioral Modeling of Steady-State Oscillators with Buffers Using Neural Networks*," 2018 IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS),* 2018, pp. 307-309, doi: 10.1109/EPEPS.2018.8534238.

M. A. Dolatsara, H. Yu, J. A. Hejase, W. D. Becker and M. Swaminathan, "Polynomial Chaos modeling for jitter estimation in high-speed links," *2018 IEEE International Test Conference (ITC)*, 2018, pp. 1-10, doi: 10.1109/TEST.2018.8624875. **INDUSTRY CO-AUTHOR**

B. Li, P. Franzen, Y. Choi and C. Cheng, "Receiver Behavior Modeling Based on System Identification," *2018 IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS),* 2018, pp. 299-301, doi: 10.1109/EPEPS.2018.8534310. **INDUSTRY CO-AUTHOR**

H. M. Torun and M. Swaminathan, “A New Machine Learning Approach for Optimization and Tuning of Integrated Systems”, *DesignCon*, Santa Clara, CA, 2018.

H. M. Torun and M. Swaminathan, "Bayesian Framework for Optimization of Electromagnetics Problems," *2018 International Workshop on Computing, Electromagnetics, and Machine Intelligence (CEMi)*, 2018, pp. 1-2, doi: 10.1109/CEMI.2018.8610600.

D. Das, S. Maity, S. B. Nasir, S. Ghosh, A. Raychowdhury and S. Sen, "ASNI: Attenuated Signature Noise Injection for Low-Overhead Power Side-Channel Attack Immunity," in *IEEE Transactions on Circuits and Systems I: Regular Papers*, vol. 65, no. 10, pp. 3300-3311, Oct. 2018, doi: 10.1109/TCSI.2018.2819499

K. Roy, H. T. Mert and M. Swaminathan, "Preliminary Application of Deep Learning to Design Space Exploration," *2018 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS),* 2018, pp. 1-3, doi: 10.1109/EDAPS.2018.8680888.

M. Ahadi, A. Varma, K. Keshavan, M. Swaminathan “Design Space Exploration with Polynomial Chaos Surrogate Models for Analyzing Large System Designs,” *DesignCon*, Jan. 2019.

E. Rosenbaum, Z. Chen, J. Xiong, “Component Modeling for System-level ESD Simulation,” *InCompliance,* vol. 11, no. 4, April 2019, pp. 16-19

A. Yang, A. Ghassami, E. Rosenbaum, and N. Kiyavash, “Data-Driven Reliability for Datacenter Hard Disk Drives,” *Electronic Device Failure Analysis Magazine*, vol. 21, no. 2, May 2019, pp. 16-21.

B. Tzen and M.Raginsky, “Theoretical Guarantees for Sampling and Inference in Generative Models with Latent Diffusions”, *COLT 2019*, doi 10.48550/arXiv.1903.01608.

M. Ahadi Dolatsara, J. A. Hejase, W. D. Becker and M. Swaminathan, "A Hybrid Methodology for Jitter and Eye Estimation in High-Speed Serial Channels Using Polynomial Chaos Surrogate Models," *IEEE Access*, vol. 7, pp. 53629-53640, 2019, doi: 10.1109/ACCESS.2019.2908799. **INDUSTRY CO-AUTHOR**

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)*, 2019, pp. 304-310, doi: 10.1109/ISQED.2019.8697576.

M. Ahadi, J. Hejase, W. Becker, M. Swaminathan “Eye Diagram and Jitter Estimation in SerDes Designs using Surrogate Models Generated with Polynomial Chaos Theory,” *DesignCon*, Jan. 2019. **INDUSTRY CO-AUTHOR**

H. Yu, J. Shin, T. Michalka, M. Larbi and M. Swaminathan, "Behavioral Modeling of Tunable I/O Drivers with Pre-emphasis Using Neural Networks," *20th International Symposium on Quality Electronic Design (ISQED)*, 2019, pp. 247-252, doi: 10.1109/ISQED.2019.8697597. **INDUSTRY CO-AUTHOR**

M. A. Dolatsara, A. Varma, K. Keshavan and M. Swaminathan, "A Modified Polynomial Chaos Modeling Approach for Uncertainty Quantification," *2019 International Applied Computational Electromagnetics Society Symposium (ACES)*, 2019, pp. 1-2.

M. Larbi, H. M. Torun and M. Swaminathan, "Estimation of Parameter Variability for High Dimensional Microwave Problems via Partial Least Squares," *2019 IEEE MTT-S International Microwave Symposium (IMS)*, 2019, pp. 940-943, doi: 10.1109/MWSYM.2019.8700749.

D. Das, A. Golder, J. Danial, S. Ghosh, A. Raychowdhury and S. Sen, "X-DeepSCA: Cross-Device Deep Learning Side Channel Attack," *2019 56th ACM/IEEE Design Automation Conference (DAC)*, 2019, pp. 1-6.

A. Golder, D. Das, J. Danial, S. Ghosh, S. Sen and A. Raychowdhury, *“*Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack,”* IEEE Transactions on Very Large Scale Integration (VLSI) Systems*, July 2019, doi 10.1109/TVLSI2019.2926324.

A. Agnesina, E. Lepercq, J. Escobedo and S. K. Lim, "Reducing Compilation Effort in Commercial FPGA Emulation Systems Using Machine Learning," *2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)*, 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942091.

H. Liu, Y. Zhou, A. Beirami, and D. Baron, “Nonlinear function estimation with empirical Bayes and approximate message passing,” *57 ^{th} Allerton Conf. Communication, Control, & Computing*, Sep. 2019, doi 10.48550/arXiv/1907.02482.

Y. -C. Lu, J. Lee, A. Agnesina, K. Samadi and S. K. Lim, "GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization," *2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)*, 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942063.

H. M. Torun, A. C. Durgun, K. Aygün and M. Swaminathan, "Enforcing Causality and Passivity of Neural Network Models of Broadband S-Parameters," *2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)*, 2019, pp. 1-3, doi: 10.1109/EPEPS47316.2019.193234.

H. Ma, E. -P. Li, A. C. Cangellaris and X. Chen, "Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links," 2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2019, pp. 1-3, doi: 10.1109/EPEPS47316.2019.193199.

H. M. Torun *et al*., "A Spectral Convolutional Net for Co-Optimization of Integrated Voltage Regulators and Embedded Inductors," *2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)*, 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942109.

O. W. Bhatti and M. Swaminathan, "Impedance Response Extrapolation of Power Delivery Networks using Recurrent Neural Networks," *2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS),* 2019, pp. 1-3, doi: 10.1109/EPEPS47316.2019.193198.

J. Hanson and M. Raginsky, “Universal approximation of input-output maps by temporal convolutional nets”, *Neural Information Processing Systems (NeurIPS),* Vancouver, Canada, December 8-14, 2019, p 14071-14081, doi 10.48550/14Xiv.1906.09211.

A. Balakir, A. Yang and E. Rosenbaum, "An Interpretable Predictive Model for Early Detection of Hardware Failure," *2020 IEEE International Reliability Physics Symposium (IRPS)*, 2020, pp. 1-5, doi: 10.1109/IRPS45951.2020.9129615.

H. Ma, E. -P. Li, A. C. Cangellaris and X. Chen, "Support Vector Regression-Based Active Subspace (SVR-AS) Modeling of High-Speed Links for Fast and Accurate Sensitivity Analysis," in *IEEE Access*, vol. 8, pp. 74339-74348, 2020, doi: 10.1109/ACCESS.2020.2988088.

J. Hanson and M. Raginsky, “Universal simulation of stable dynamical systems by recurrent neural nets”, *Proceedings of the 2nd Conference on Learning for Dynamics and Control (L4DC)*, PMLR 120:384-392, June, 2020.

Y. -C. Lu, S. S. Kiran Pentapati, L. Zhu, K. Samadi and S. K. Lim, "TP-GNN: A Graph Neural Network Framework for Tier Partitioning in Monolithic 3D ICs," *2020 57th ACM/IEEE Design Automation Conference (DAC)*, 2020, pp. 1-6, doi: 10.1109/DAC18072.2020.9218582.

H. Ma, E-P. Li, A.C. Cangellaris, X. Chen, “Expedient Prediction of Eye Opening of High-Speed Links with Input Design Space Dimensionality Reduction,” *Proc 2020 IEEE International Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMCSI),* Reno, NV, July 2020, doi: 10.1109/EMCSI38923.2020.9191544

A. Yang, A-E.Ghassami, M. Raginsky, N. Kiyavash, and E. Rosenbaum, “Model-Augmented Conditional Mutual Information Estimation for Feature Selection”, *Proceedings of Machine Learning Research*, August 3-6, 2020.

M. Swaminathan, H. M. Torun, H. Yu, J. A. Hejase and W. D. Becker, "Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging," in *IEEE Transactions on Components, Packaging and Manufacturing Technology*, vol. 10, no. 8, pp. 1276-1295, Aug. 2020, doi: 10.1109/TCPMT.2020.3011910.

H. M. Torun, A. C. Durgun, K. Aygün and M. Swaminathan, "Causal and Passive Parameterization of S-Parameters Using Neural Networks," *IEEE Transactions on Microwave Theory and Techniques*, vol. 68, no. 10, pp. 4290-4304, Oct. 2020, doi: 10.1109/TMTT.2020.3011449.

F. Aydin, P. Kashyap, S Potluri, P. Franzon, A. Aysu, “DeePar-SCA: Breaking Parallel Architectures of Lattice Cryptography via Learning Based Side-Channel Attacks”, *International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS)*, October 7, 2020.

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.

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, doi: 10.1109/TCAD.2020.3038701.

L. Francisco *et al*., "Design Rule Checking with a CNN Based Feature Extractor," *2020 ACM/IEEE 2nd Workshop on Machine Learning for CAD (MLCAD)*, 2020, pp. 9-14, doi: 10.1145/3380446.3430625.

I. Turtletaub, M. Ibrahim, G. Li, and P. Franzon, “Application of Quantum Machine Learning to VLSI Placement,” *MLCAD ’20: Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD*, Virtual Event Iceland, November 2020, pp, 61-66.

Rosenbaum, J. Xiong, A. Yang, Z. Chen and M. Raginsky, “Machine Learning for Circuit Aging Simulation,” *2020 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2020*, pp. 39.1.1-39.1.4, doi: 10.1109/IEDM13553.2020.9371931.

Y-C Lu, S. Nath, S. S. Kiran Pentapati and S. K. Lim, “A Fast Learning-Driven Signoff Power Optimization Framework,” *2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD)*, San Diego, CA, USA, 2020, pp. 1-9.** INDUSTRY CO-AUTHOR**

H. Huang, A. C. Cangellaris and X. Chen, “Stochastic-Galerkin Finite-Difference Time-Domain for Waves in Random Layered Media,” *2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO),* Hangzhou, China, 2020, pp. 1-4, doi: 10.1109/NEMO49486.2020.9343635.

Y-C Lu, S. Pentapati, and S. K. Lim. 2021, “The Law of Attraction: Affinity-Aware Placement Optimization using Graph Neural Networks”, *Proceedings of the 2021 International Symposium on Physical Design (ISPD ‘21*), Association for Computing Machinery, New York, NY, USA, 7–14. DOI:https://doi.org/10.1145/3439706.3447045

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),* Aug 30 – Sept 3, 2021, pp. 1-6, doi: 10.1109/MLCAD52597.2021.9531109.

J. Xiong, Z. Chen, M. Raginsky and E. Rosenbaum, “Statistical Learning of IC Models for System-Level ESD Simulation,”*IEEE Transactions on Electromagnetic Compatibility,* vol. 63, no. 5, pp. 1302-1311, Oct. 2021, doi: 10.1109/TEMC.2021.3076492.

O.W. Bhatti, H. M. Torun and M. Swaminathan, “HilbertNet: A Probabilistic Machine Learning Framework for Frequency Response Extrapolation of Electromagnetic Structures,” in *IEEE Transactions on Electromagnetic Compatibility*, Nov 24, 2021, p 1-13. doi: 10.1109/TEMC.2021.3119277.

G. Aydin & E. Karabulut, & S. Potluri, & E. Alkim & A. Aysu, “RevEAL: Single-Trace Side-Channel Leakage of the SEAL Homomorphic Encryption Library”, *2022 Design, Automation and Test in Europe (DATE)*, December 2021.

Y-C. Lu, S. Nath, V. Khandelwal, and S.K. Lim, “RL-Sizer: VLSI Gate Sizing for Timing Optimization using Deep Reinforcement Learning*”, 58 ^{th} ACM/IEEE Design Automation Conference (DAC)*, Dec 5-9, 2021. 10.1109/DAC18074.2021.9586138

**INDUSTRY CO-AUTHOR**

O.W. Bhatti, N. Ambasana and M. Swaminathan, “Inverse Design of Power Delivery Networks using Invertible Neural Networks,” *2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS),* 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609211.

N. Ambasana *et al*., “Invertible Neural Networks for High-Speed Channel Design & Parameter Distribution Estimation,” *2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS),* 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609225.

X-J. Shangguan, H. Ma, A. C. Cangellaris and X. Chen, “Effect of Sampling Method on the Regression Accuracy for a High-Speed Link Problem*,” 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS),* 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609130.

O. W. Bhatti et al., “Comparison of Invertible Architectures for High Speed Channel Design,” *2021 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS)*, 2021, pp. 1-3, doi: 10.1109/EDAPS53774.2021.9657014. **BEST STUDENT PAPER AWARD**

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),* 2021, pp. 1-3, doi: 10.1109/EPEPS51341.2021.9609124.

J. Xiong, A. S. Yang, M. Raginsky and E. Rosenbaum, "Neural Networks for Transient Modeling of Circuits : Invited Paper," *2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)*, 2021, pp. 1-7, doi: 10.1109/MLCAD52597.2021.9531153.

O.W. Bhatti and M. Swaminathan, “Design Space Extrapolation for Power Delivery Networks using a Transposed Convolutional Net”, *22nd International Symposium on Quality Electronic Design (ISQED’21)* 2021, pp. 7-12, doi: 10.1109/ISQED51717.2021.9424309.

P. Kashyap et al., "Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks," *2022 IEEE 72nd Electronic Components and Technology Conference (ECTC*), 2022, pp. 1958-1963, doi: 10.1109/ECTC51906.2022.00307.

A. Yang, J. Xiong, M. Raginsky, & E. Rosenbaum, "Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits," *4th Annual Conference on Learning for Dynamics and Control, Proceedings of Machine Learning Research, 2022*, vol 168, p1-13, doi: 10.48550/arXiv.2202.06453

M. Swaminathan, O. W. Bhatti, Y. Guo, E. Huang and O. Akinwande, "Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design," in *IEEE Transactions on Microwave Theory and Techniques*, 2022, doi: 10.1109/TMTT.2022.3206455.

A. Page and X. Chen, "Efficient Uncertainty Quantification of Stripline Pulse Response using Singular Value Decomposition and Delay Extraction," *2022 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC)*, 2022, pp. 4-6, doi: 10.1109/APEMC53576.2022.9888395.

H. Ma *et al*., "Channel Inverse Design Using Tandem Neural Network," *2022 IEEE 26th Workshop on Signal and Power Integrity (SPI)*, 2022, pp. 1-3, doi: 10.1109/SPI54345.2022.9874935.

J. Xiong, A. Yang, M. Raginsky and E. Rosenbaum, "Neural Ordinary Differential Equation Models of Circuits: Capabilities and Pitfalls," in *IEEE Transactions on Microwave Theory and Techniques*, 2022, doi: 10.1109/TMTT.2022.3208896.

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),* 2022, pp. 1-9, doi: 10.1109/FCCM53951.2022.9786085 **INDUSTRY CO-AUTHOR**