Optimizing Grouped Convolutions on Edge Devices. - Perry Gibson; José Cano 001; Jack Turner; Elliot J. Crowley; Michael F. P. O'Boyle; Amos J. Storkey (ASAP, 2020)

Orpheus: A New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference. - Perry Gibson; José Cano 001 (ISPASS, 2020)

SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference. - Jude Haris; Perry Gibson; José Cano 001; Nicolas Bohm Agostini; David R. Kaeli (SBAC-PAD, 2021)

Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation. - Perry Gibson; José Cano 001 (PACT, 2022)

Bifrost: End-to-End Evaluation and optimization of Reconfigurable DNN Accelerators. - Axel Stjerngren; Perry Gibson; José Cano 001 (ISPASS, 2022)

Reusing Auto-Schedules for Efficient DNN Compilation. - Perry Gibson; José Cano 001 (CoRR, 2022)

Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection. - Perry Gibson; José Cano 001 (CoRR, 2022)

Exploring Effects of Computational Parameter Changes to Image Recognition Systems. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (CoRR, 2022)

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (ICSME, 2023)

Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (ASE, 2023)

MutateNN: Mutation Testing of Image Recognition Models Deployed on Hardware Accelerators. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (CoRR, 2023)

Fault Localization for Framework Conversions of Image Recognition Models. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (CoRR, 2023)

A Differential Testing Framework to Evaluate Image Recognition Model Robustness. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (CoRR, 2023)

DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack. - Perry Gibson; José Cano 001; Elliot J. Crowley; Amos J. Storkey; Michael F. P. O'Boyle (CoRR, 2023)

Fix-Con: Automatic Fault Localization and Repair of Deep Learning Model Conversions. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (CoRR, 2023)

SECDA-TFLite: A toolkit for efficient development of FPGA-based DNN accelerators for edge inference. - Jude Haris; Perry Gibson; José Cano 001; Nicolas Bohm Agostini; David R. Kaeli (J. Parallel Distributed Comput., 2023)

AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators. - Nicolas Bohm Agostini; Jude Haris; Perry Gibson; Malith Jayaweera; Norm Rubin; Antonino Tumeo; José L. Abellán; José Cano 001; David R. Kaeli (CGO, 2024)

Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs. - Aidan Ferguson; Perry Gibson; Lara D'Agata; Parker McLeod; Ferhat Yaman; Amitabh Das; Ian Colbert; José Cano 001 (EuroMLSys, 2025)

Exploring Robustness of Image Recognition Models on Hardware Accelerators. - Nikolaos Louloudakis; Perry Gibson; José Cano 001; Ajitha Rajan (ICSTW, 2025)

DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration. - Perry Gibson; José Cano 001; Elliot Crowley; Amos J. Storkey; Michael F. P. O'Boyle (ACM Trans. Archit. Code Optim., 2025)