Analysis and improvement of resilience for long short-term memory neural networksAhmadilivani, Mohammad Hasan; Raik, Jaan; Daneshtalab, Masoud; Kuusik, Alar2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)2023 https://doi.org/10.1109/DFT59622.2023.10313559

APPRAISER : DNN fault resilience analysis employing approximation errorsTaheri, Mahdi; Ahmadilivani, Mohammad Hasan; Jenihhin, Maksim; Raik, Jaan; Daneshtalab, Masoud26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems, May 3-5, 2023, Tallinn2023 / p. [?] https://ddecs2023.taltech.ee/

DeepAxe : a framework for exploration of approximation and reliability trade-offs in DNN acceleratorsTaheri, Mahdi; Riazati, Mohamad; Ahmadilivani, Mohammad Hasan; Jenihhin, Maksim; Daneshtalab, Masoud; Raik, Jaan; Sjödin, Mikael; Lisper, BjörnarXiv.org2023 / 8 p. : ill https://doi.org/10.48550/arXiv.2303.0822

An efficient analog convolutional neural network hardware accelerator enabled by a novel memoryless architecture for insect-sized robotsDadras, Iman; Ahmadilivani, Mohammad Hasan; Banerji, Saoni; Raik, Jaan; Abloo, Alvo2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST) : Bremen, Germany : 08-10 June 20222022 / p. 1-6 https://doi.org/10.1109/MOCAST54814.2022.9837551