HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology. - Nael Fasfous; Manoj Rohit Vemparala; Alexander Frickenstein; Emanuele Valpreda; Driton Salihu; Nguyen Anh Vu Doan; Christian Unger; Naveen Shankar Nagaraja; Maurizio Martina; Walter Stechele (ACM Trans. Embed. Comput. Syst., 2021)

Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs. - Nael Fasfous; Lukas Frickenstein; Michael Neumeier; Manoj Rohit Vemparala; Alexander Frickenstein; Emanuele Valpreda; Maurizio Martina; Walter Stechele (DATE, 2022)

AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms. - Nael Fasfous; Manoj Rohit Vemparala; Alexander Frickenstein; Emanuele Valpreda; Driton Salihu; Julian Höfer; Anmol Singh; Naveen Shankar Nagaraja; Hans-Jörg Vögel; Nguyen Anh Vu Doan; Maurizio Martina; Jürgen Becker 001; et al. (DATE, 2022)

NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators. - Giuseppe Aiello; Beatrice Bussolino; Emanuele Valpreda; Massimo Ruo Roch; Guido Masera; Maurizio Martina; Stefano Marsi (ICIP, 2022)

HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology. - Manoj Rohit Vemparala; Nael Fasfous; Alexander Frickenstein; Emanuele Valpreda; Manfredi Camalleri; Qi Zhao; Christian Unger; Naveen Shankar Nagaraja; Maurizio Martina; Walter Stechele (Leibniz Trans. Embed. Syst., 2022)

TEMET: Truncated REconfigurable Multiplier with Error Tuning. - Flavia Guella; Emanuele Valpreda; Michele Caon; Guido Masera; Maurizio Martina (ApplePies, 2023)

ERODE: Error Resilient Object DetEction by Recovering Bounding Box and Class Information. - Emanuele Valpreda; Giuseppe Palumbo; Michele Caon; Guido Masera; Maurizio Martina (PRIME, 2023)

Implementation and integration of NTT/INTT accelerator on RISC-V for CRYSTALS-Kyber. - Alessandra Dolmeta; Emanuele Valpreda; Maurizio Martina; Guido Masera (CF (Companion), 2024)

Hardware/Neural Network Codesign for Energy-Efficient Inference on Edge Devices with Optimal Mapping and Compression. - Emanuele Valpreda (2024)

MARLIN: A Co-Design Methodology for Approximate ReconfigurabLe Inference of Neural Networks at the Edge. - Flavia Guella; Emanuele Valpreda; Michele Caon; Guido Masera; Maurizio Martina (IEEE Trans. Circuits Syst. I Regul. Pap., 2024)