Superstate identification for state machines using search-based clustering. - Mathew Hall; Phil McMinn; Neil Walkinshaw (GECCO, 2010)

Search based hierarchy generation for reverse engineered state machines. - Mathew Hall (SIGSOFT FSE, 2011)

Complexity Metrics for Hierarchical State Machines. - Mathew Hall (SSBSE, 2011)

Supervised software modularisation. - Mathew Hall; Neil Walkinshaw; Phil McMinn (ICSM, 2012)

Using Behaviour Inference to Optimise Regression Test Sets. - Ramsay Taylor; Mathew Hall; Kirill Bogdanov 002; John Derrick (ICTSS, 2012)

Improving software remodularisation. - Mathew Hall (2013)

Automating the Design of Processor/Accelerator Embedded Systems with LegUp High-Level Synthesis. - Blair Fort; Andrew Canis; Jongsok Choi; Nazanin Calagar; Ruolong Lian; Stefan Hadjis; Yu Ting Chen; Mathew Hall; Bain Syrowik; Tomasz S. Czajkowski; Stephen Brown 003; Jason Helge Anderson (EUC, 2014)

Establishing the Source Code Disruption Caused by Automated Remodularisation Tools. - Mathew Hall; Muhammad Ali Khojaye; Neil Walkinshaw; Phil McMinn (ICSME, 2014)

Data and Analysis Code for GP EFSM Inference. - Mathew Hall; Neil Walkinshaw (ICSME, 2016)

Inferring Computational State Machine Models from Program Executions. - Neil Walkinshaw; Mathew Hall (ICSME, 2016)

Effectively Incorporating Expert Knowledge in Automated Software Remodularisation. - Mathew Hall; Neil Walkinshaw; Phil McMinn (IEEE Trans. Software Eng., 2018)

HPIPE: Heterogeneous Layer-Pipelined and Sparse-Aware CNN Inference for FPGAs. - Mathew Hall; Vaughn Betz (FPGA, 2020)

Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs. - Andrew Boutros; Mathew Hall; Nicolas Papernot; Vaughn Betz (FPT, 2020)

From TensorFlow Graphs to LUTs and Wires: Automated Sparse and Physically Aware CNN Hardware Generation. - Mathew Hall; Vaughn Betz (FPT, 2020)

End-to-End FPGA-based Object Detection Using Pipelined CNN and Non-Maximum Suppression. - Anupreetham Anupreetham; Mohamed Ibrahim; Mathew Hall; Andrew Boutros; Ajay Kuzhively; Abinash Mohanty; Eriko Nurvitadhi; Vaughn Betz; Yu Cao 001; Jae-sun Seo (FPL, 2021)

HPIPE NX: Boosting CNN Inference Acceleration Performance with AI-Optimized FPGAs. - Marius Stan; Mathew Hall; Mohamed Ibrahim; Vaughn Betz (FPT, 2022)

Extending Data Flow Architectures for Convolutional Neural Networks to Multiple FPGAs. - Mohamed Ibrahim; Zhipeng Zhao; Mathew Hall; Vaughn Betz (ICFPT, 2023)

With Shared Microexponents, A Little Shifting Goes a Long Way. - Bita Darvish Rouhani; Ritchie Zhao; Venmugil Elango; Rasoul Shafipour; Mathew Hall; Maral Mesmakhosroshahi; Ankit More; Levi Melnick; Maximilian Golub; Girish Varatkar; Lai Shao; Gaurav Kolhe; et al. (ISCA, 2023)

Shared Microexponents: A Little Shifting Goes a Long Way. - Bita Rouhani; Ritchie Zhao; Venmugil Elango; Rasoul Shafipour; Mathew Hall; Maral Mesmakhosroshahi; Ankit More; Levi Melnick; Maximilian Golub; Girish Varatkar; Lei Shao; Gaurav Kolhe; et al. (CoRR, 2023)

Microscaling Data Formats for Deep Learning. - Bita Darvish Rouhani; Ritchie Zhao; Ankit More; Mathew Hall; Alireza Khodamoradi; Summer Deng; Dhruv Choudhary; Marius Cornea; Eric Dellinger; Kristof Denolf; Dusan Stosic; Venmugil Elango; et al. (CoRR, 2023)

H2PIPE: High Throughput CNN Inference on FPGAs with High-Bandwidth Memory. - Mario Doumet; Marius Stan; Mathew Hall; Vaughn Betz (FPL, 2024)

High Throughput FPGA-Based Object Detection via Algorithm-Hardware Co-Design. - Anupreetham Anupreetham; Mohamed Ibrahim; Mathew Hall; Andrew Boutros; Ajay Kuzhively; Abinash Mohanty; Eriko Nurvitadhi; Vaughn Betz; Yu Cao 001; Jae-Sun Seo (ACM Trans. Reconfigurable Technol. Syst., 2024)