2026
Alex Chan, Moh Tarraf, Rishad Shafik and Alex Yakovlev, “ANIMATE: Automated Framework for Scalable Design of Tsetlin Machines Using 1-Safe Petri Nets”,
Proc. Application and Theory of Petri Nets and Concurrency, pp. 130-153, Jun 2026.
Chang Sun, Zhiqiang Que, Thea K. Årrestad, Vladimir Loncar, Jennifer Ngadiuba, Wayne Luk, Maria Spiropulu, “HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs_”, FPGA, Feb 2026.
Olga Tarasyuk, Anatoliy Gorbenko, Tousif Rahman, Lei Jiao, Ole-Christoffer Granmo, Rishad Shafik, Alex Yakovlev, “Learning dynamics, pattern recognition capability and interpretability of the Tsetlin Machine”, Pattern Recognition, Volume 174, June 2026.
Ce Guo, Tong Zhao, “ResBench: A Resource-Aware Benchmark for LLM-Generated FPGA Designs”, HEART, pp 25-34, June 2026.
Hiroaki Ito, Hikari Otsuka, Ryota Yasudo, Zhiqiang Que, Jose G. F. Coutinho, Daichi Fujiki, Masato Motomura, Ce Guo, Wayne Luk, “Memory-Efficient and Trustworthy Neural Networks via Random Seed-Based Design”, IEEE Access, Vol 14, Jan 2026.
(Best Paper Award) C. Sun et al., “HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference”, IEEE Symposium on Field-Programmable Custom Computing Machines, May 2026.
2025
(Best Paper Award) Zhiqiang Que, Chang Sun, Sudarshan Paramesvaran, Emyr Clement, Katerina Karakoulaki, Christopher Brown, Lauri Laatu, Arianna Cox, Alexander Tapper, Wayne Luk, Maria Spiropulu, “JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs”, FPT, Dec 2025.
Angus T. L. Leung, Ce Guo, and Wayne Luk, “FABS: An Extensible and High-Performance Digital Twin Framework of AI-Driven Financial Systems”, Proc. ACM International Conference on AI in Finance, 27-34, Nov 2025.
Lauri Laatu, Chang Sun, Arianna Cox, Abhijith Gandrakota, Benedikt Maier, Jennifer Ngadiuba, Zhiqiang Que, Wayne Luk, Maria Spiropulu, and Alexander Tapper, “Sub-microsecond Transformers for Jet Tagging on FPGAs, ML and the Physical Sciences”, NeurIPS 2025 Workshop, Nov 2025.
(Best Paper Award) Bob Pattison, Tousif Rahman, Alex Chan, Ekin Can Erkuş, Kabita Adhikari, Ole-Christoffer Granmo, David Thomas, Alex Yakovlev, Rishad Shafik, “TMAtlas: An Interactive Visual Analytics Framework for Explaining Tsetlin Machine Outputs”, Proceedings ISTM, Oct 2025.
Ekin Can Erkus, Alex Chan, Walter Distaso, David Thomas, Alex Yakovlev, Rishad Shafik, “LatentGraph: From Latent States to Rule-based Expressions for Explainable Financial Forecasting”, Proc. AI in Finance, pp 745-752, Nov 2025.
C. Xu, S. Duan, R. Shafik and A. Yakovlev, “Recurrent Tsetlin Machine for Sequence Learning” 2025 International Symposium on the Tsetlin Machine (ISTM), pp. 102-106, Oct 2025.
Y. Zeng, S. Duan, R. Shafik and A. Yakovlev, “Fast and Compact Tsetlin Machine Inference on CPUs Using Instruction-Level Optimization” 2025 International Symposium on the Tsetlin Machine (ISTM) pp. 44-47, Oct 2025.
Anatoliy Gorbenko, Olga Tarasyuk, Jingjing Zhang, Rishad Shafik, Alex Yakovlev, Matthias Eberl, “Using Tsetlin Machine for Decoding, Visualization and Minimization of Local Immune Fingerprints in Peritoneal Dialysis Infections”, 2025 International Symposium on the Tsetlin Machine (ISTM), Oct 2025.
Saram Abbas; Naeem Soomro; Ole-Christoffer Granmo; Rishad Shafik; Rakesh Heer; Kabita Adhikari, “AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines”, 2025 International Symposium on the Tsetlin Machine (ISTM), Oct 2025.
Ziyang Jiao, Ce Guo, Wayne Luk, “Scalable Time Series Causal Discovery with Approximate Causal Ordering”, Mathematics, vol. 13, no. 20, Oct 2025.
Lisa Faloughi, Ce Guo, Wayne Luk, “ProtoHedge: Interpretable Hedging with Market Prototypes”, Proc. ACM International Conference on AI in Finance, pp 202-210, Oct 2025.
S. Duan, R. Shafik and A. Yakovlev, “ETHEREAL: Energy-efficient and High-throughput Inference using Compressed Tsetlin Machine.”, IWASI, Aug 2025.
Saram Abbas, Naeem Soomro, Rishad Shafik, Rakesh Heer, Kabita Adhikari, “Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction”, Proc. IEEE Cong. Engineering in Medicine and Biology, pp 1-7, Jul 2025.
Olga Tarasyuk, Anatoliy Gorbenko, Matthias Eberl, Nicholas Topley, Jingjing Zhang, Rishad Shafik, Alex Yakovlev, “Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines”, Bioinformatics Advances, Vol 5, No. 1, Jun 2025.
G. Mao, T. Rahman, S. Maheshwari, B. Pattison, Z. Shao, R. Shafik, and A. Yakovlev, “Dynamic Tsetlin Machine Accelerators for On-Chip Training Using FPGAs”, IEEE Transactions on Circuits and Systems, pp 1-14, May 2025.
Zhiqiang Que, Hongxiang Fan, Gabriel Figueiredo, Ce Guo, Wayne Luk, Ryota Yasudo, Masato Motomura, “Trustworthy Deep Learning Acceleration with Customizable Design Flow Automation”, HEART, pp 1-13, May 2025.
Victor Khomenko, Maciej Koutny, and Alex Yakovlev, “Distributed Places and Safe Net Reduction”,
Application and Theory of Petri Nets and Concurrency (PETRI NETS), pp. 265-286, Jun 2025.
S. Abbas, R. Shafik, N. Soomro, R. Heer and K. Adhikari, “AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses”, Frontiers in Oncology, Vol 14, Jan 2025.
2024
A. Chan, A. Wheeldon, R. Shafik and A. Yakovlev, “Design of Event-Driven Tsetlin Machines Using Safe Petri Nets.”,” in “International Conference on Applications and Theory of Petri Nets and Concurrency”, pp. 357-378, 2024.
Z. Que, M. Zhang, H. Fan, H. Li, C. Guo and W. Luk, “Low Latency Variational Autoencoder on FPGAs,” in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 14, no. 2, pp. 323-333, 2024.
Q. Wang, Z. Que and W. Luk, “Trustworthy Codesign by Verifiable Transformations” 2024 IEEE International Test Conference in Asia (ITC-Asia), pp. 1-6, 2024.
C. Guo, H. Wu, and W. Luk, “Resource-Constraint Bayesian Optimization for Soft Processors on FPGAs”, Proceedings of the 14th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies, pp. 27-36, 2024.
Z. Zhang, H. Fan,H. Chen, L. Dudziak,and W. Luk, “Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA”, Proceedings of the 61st ACM/IEEE Design Automation Conference, article no. 301, 2024.
J. Vandebon, J. G. Coutinho and W. Luk, “Auto-Generating Diverse Heterogeneous Designs”, IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 116-123, 2024.