Federated Learning Based on Dynamic Regularization. - Durmus Alp Emre Acar; Yue Zhao; Ramon Matas Navarro; Matthew Mattina; Paul N. Whatmough; Venkatesh Saligrama (ICLR, 2021)

Debiasing Model Updates for Improving Personalized Federated Training. - Durmus Alp Emre Acar; Yue Zhao; Ruizhao Zhu; Ramon Matas Navarro; Matthew Mattina; Paul N. Whatmough; Venkatesh Saligrama (ICML, 2021)

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. - Colby R. Banbury; Chuteng Zhou; Igor Fedorov; Ramon Matas Navarro; Urmish Thakker; Dibakar Gope; Vijay Janapa Reddi; Matthew Mattina; Paul N. Whatmough (MLSys, 2021)

Collapsible Linear Blocks for Super-Efficient Super Resolution. - Kartikeya Bhardwaj; Milos Milosavljevic; Liam O'Neil; Dibakar Gope; Ramon Matas Navarro; Alex Chalfin; Naveen Suda; Lingchuan Meng; Danny Loh (MLSys, 2022)

UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. - Igor Fedorov; Ramon Matas Navarro; Hokchhay Tann; Chuteng Zhou; Matthew Mattina; Paul N. Whatmough (NeurIPS, 2022)

UDC: Unified DNAS for Compressible TinyML Models. - Igor Fedorov; Ramon Matas Navarro; Hokchhay Tann; Chuteng Zhou; Matthew Mattina; Paul N. Whatmough (CoRR, 2022)

PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices. - Yuji Chai; Devashree Tripathy; Chuteng Zhou; Dibakar Gope; Igor Fedorov; Ramon Matas Navarro; David Brooks 001; Gu-Yeon Wei; Paul N. Whatmough (CoRR, 2023)