A Tunable Dual-Edge Time-to-Digital Converter. - Colin Drewes; Steven Harris; Winnie Wang; Richard Appen; Olivia Weng; Ryan Kastner; William Hunter; Christopher McCarty; Dustin Richmond (FCCM, 2021)

Hardware-efficient Residual Networks for FPGAs. - Olivia Weng; Alireza Khodamoradi; Ryan Kastner (CoRR, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (CoRR, 2021)

Neural Network Quantization for Efficient Inference: A Survey. - Olivia Weng (CoRR, 2021)

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark. - Hendrik Borras; Giuseppe Di Guglielmo; Javier M. Duarte; Nicolò Ghielmetti; Benjamin Hawks; Scott Hauck; Shih-Chieh Hsu; Ryan Kastner; Jason Liang; Andres Meza 001; Jules Muhizi; Tai Nguyen 003; et al. (CoRR, 2022)

Turn on, Tune in, Listen up: Maximizing Side-Channel Recovery in Time-to-Digital Converters. - Colin Drewes; Olivia Weng; Keegan Ryan; Bill Hunter; Christopher McCarty; Ryan Kastner; Dustin Richmond (FPGA, 2023)

Adapting Skip Connections for Resource-Efficient FPGA Inference. - Olivia Weng; Gabriel Marcano; Vladimir Loncar; Alireza Khodamoradi; Nojan Sheybani; Farinaz Koushanfar; Kristof Denolf; Javier Mauricio Duarte; Ryan Kastner (FPGA, 2023)

Tailor: Altering Skip Connections for Resource-Efficient Inference. - Olivia Weng; Gabriel Marcano; Vladimir Loncar; Alireza Khodamoradi; Nojan Sheybani; Farinaz Koushanfar; Kristof Denolf; Javier Mauricio Duarte; Ryan Kastner (CoRR, 2023)

Pentimento: Data Remanence in Cloud FPGAs. - Colin Drewes; Olivia Weng; Andres Meza 001; Alric Althoff; David Kohlbrenner; Ryan Kastner; Dustin Richmond (ASPLOS (2), 2024)

Reliable edge machine learning hardware for scientific applications. - Tommaso Baldi; Javier Campos; Benjamin Hawks; Jennifer Ngadiuba; Nhan Tran; Daniel Diaz 003; Javier M. Duarte; Ryan Kastner; Andres Meza 001; Melissa Quinnan; Olivia Weng; Caleb Geniesse; et al. (VTS, 2024)

Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications. - Olivia Weng; Alexander Redding; Nhan Tran; Javier Mauricio Duarte; Ryan Kastner (CoRR, 2024)

Tailor: Altering Skip Connections for Resource-Efficient Inference. - Olivia Weng; Gabriel Marcano; Vladimir Loncar; Alireza Khodamoradi; G. Abarajithan; Nojan Sheybani; Andres Meza 001; Farinaz Koushanfar; Kristof Denolf; Javier Mauricio Duarte; Ryan Kastner (ACM Trans. Reconfigurable Technol. Syst., 2024)

Turn on, Tune in, and Listen up: Maximizing Side-Channel Recovery in Cross-Platform Time-to-Digital Converters. - Colin Drewes; Tyler Sheaves; Olivia Weng; Keegan Ryan; Bill Hunter; Christopher McCarty; Ryan Kastner; Dustin Richmond (ACM Trans. Reconfigurable Technol. Syst., 2024)

Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT. - Olivia Weng; Marta Andronic; Danial Zuberi; Jiaqing Chen; Caleb Geniesse; George A. Constantinides; Nhan Tran; Nicholas J. Fraser; Javier Mauricio Duarte; Ryan Kastner (FPGA, 2025)

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing. - Tommaso Baldi; Javier Campos; Olivia Weng; Caleb Geniesse; Nhan Tran; Ryan Kastner; Alessandro Biondi 001 (CoRR, 2025)