TEAM 21: A Framework for the Total Enterprise. - D. Charles Hair; Timothy Martin; Yanhe Jin (WebNet, 1999)

Evaluation of bluetooth low power for physiological monitoring in a home based cardiac rehabilitation program. - Timothy Martin; Hang Ding; Matthew D'Souza; Mohan Karunanithi (HIC, 2012)

Machine-Learning Based Congestion Estimation for Modern FPGAs. - Dani Maarouf; Abeer Alhyari; Ziad Abuowaimer; Timothy Martin; Andrew David Gunter; Gary Gréwal; Shawki Areibi; Anthony Vannelli (FPL, 2018)

GPlace3.0: Routability-Driven Analytic Placer for UltraScale FPGA Architectures. - Ziad Abuowaimer; Dani Maarouf; Timothy Martin; Jérémy Foxcroft; Gary Gréwal; Shawki Areibi; Anthony Vannelli (ACM Trans. Design Autom. Electr. Syst., 2018)

A Flat Timing-Driven Placement Flow for Modern FPGAs. - Timothy Martin; Dani Maarouf; Ziad Abuowaimer; Abeer Alhyari; Gary Gréwal; Shawki Areibi (DAC, 2019)

Novel Congestion-estimation and Routability-prediction Methods based on Machine Learning for Modern FPGAs. - Abeer Y. Al-Hyari; Ziad Abuowaimer; Timothy Martin; Gary Gréwal; Shawki Areibi; Anthony Vannelli (ACM Trans. Reconfigurable Technol. Syst., 2019)

A Deep-Learning Framework for Predicting Congestion During FPGA Placement. - Dani Maarouf; Ahmed Shamli; Timothy Martin; Gary Gréwal; Shawki Areibi (FPL, 2020)

An Adaptive Analytic FPGA Placement Framework based on Deep-Learning. - Abeer Y. Al-Hyari; Ahmed Shamli; Timothy Martin; Shawki Areibi; Gary Gréwal (MLCAD, 2020)

Machine Learning for Congestion Management and Routability Prediction within FPGA Placement. - Hannah Szentimrey; Abeer Y. Al-Hyari; Jérémy Foxcroft; Timothy Martin; David Noel; Gary William Grewal; Shawki Areibi (ACM Trans. Design Autom. Electr. Syst., 2020)

Using Machine Learning to Predict Operating Frequency During Placement in FPGA Designs. - M. Fathi; Timothy Martin; Gary Gréwal; Shawki Areibi (ICM, 2021)

A Machine Learning Approach to Predict Timing Delays During FPGA Placement. - Timothy Martin; Gary Gréwal; Shawki Areibi (IPDPS Workshops, 2021)

Effective Machine-Learning Models for Predicting Routability During FPGA Placement. - Timothy Martin; Shawki Areibi; Gary Gréwal (MLCAD, 2021)

A Deep Learning Framework to Predict Routability for FPGA Circuit Placement. - Abeer Y. Al-Hyari; Hannah Szentimrey; Ahmed Shamli; Timothy Martin; Gary Gréwal; Shawki Areibi (ACM Trans. Reconfigurable Technol. Syst., 2021)

An Adaptive Sequential Decision Making Flow for FPGAs using Machine Learning. - Timothy Martin; Charlotte Barnes; Shawki Areibi; Gary Gréwal (ICM, 2022)

Faster FPGA Routing by Forecasting and Pre-Loading Congestion Information. - Umair F. Siddiqi; Timothy Martin; Sam Van Den Eijnden; Ahmed Shamli; Gary Gréwal; Sadiq M. Sait; Shawki Areibi (MLCAD, 2022)

Integrating Machine-Learning Probes into the VTR FPGA Design Flow. - Timothy Martin; Charlotte Barnes; Gary William Grewal; Shawki Areibi (SBCCI, 2022)

Guiding FPGA Detailed Placement via Reinforcement Learning. - P. Esmaeili; Timothy Martin; Shawki Areibi; Gary Gréwal (VLSI-SoC, 2022)

A Deep-Learning Data-Driven Approach for Reducing FPGA Routing Runtimes. - Timothy Martin; Qi Li 033; Charlotte Barnes; Gary Gréwal; Shawki Areibi (ICFPT, 2023)

FPGA Placement: Dynamic Decision Making Via Machine Learning. - Timothy Martin; Charlotte Barnes; Gary William Grewal; Shawki Areibi (SBCCI, 2023)

Integrating Machine-Learning Probes in FPGA CAD: Why and How? - Timothy Martin; Charlotte Barnes; Gary Gréwal; Shawki Areibi (IEEE Des. Test, 2023)

A High-Performance Routing Engine for Large-Scale FPGAs. - Timothy Martin; Dani Maarouf; Gary Gréwal; Shawki Areibi (FPL, 2024)