Using Residue Number Systems to Accelerate Deterministic Bit-stream Multiplication. - Kamyar Givaki; Reza Hojabr; M. Hassan Najafi; Ahmad Khonsari; M. Hossein Gholamrezayi; Saeid Gorgin 001; Dara Rahmati (ASAP, 2019)

SkippyNN: An Embedded Stochastic-Computing Accelerator for Convolutional Neural Networks. - Reza Hojabr; Kamyar Givaki; S. M. Reza Tayaranian; Parsa Esfahanian; Ahmad Khonsari; Dara Rahmati; M. Hassan Najafi (DAC, 2019)

TaxoNN: A Light-Weight Accelerator for Deep Neural Network Training. - Reza Hojabr; Kamyar Givaki; Kossar Pourahmadi; Parsa Nooralinejad; Ahmad Khonsari; Dara Rahmati; M. Hassan Najafi (ISCAS, 2020)

On the Resilience of Deep Learning for Reduced-voltage FPGAs. - Kamyar Givaki; Behzad Salami 001; Reza Hojabr; S. M. Reza Tayaranian; Ahmad Khonsari; Dara Rahmati; Saeid Gorgin 001; Adrián Cristal; Osman S. Unsal (PDP, 2020)

High-Performance Deterministic Stochastic Computing Using Residue Number System. - Kamyar Givaki; Reza Hojabr; MohammadHosein Gholamrezaei; Ahmad Khonsari; Saeid Gorgin 001; Dara Rahmati; M. Hassan Najafi (IEEE Des. Test, 2021)

Hardware Efficient FIR Filter Architectures Using Accurate Unary Stochastic Computing. - Kamyar Givaki; Ahmad Khonsari; M. Hossein Gholamrezaei; Dara Rahmati; Saeid Gorgin 001 (ICCD, 2022)

A Generalized Residue Number System Design Approach for Ultralow-Power Arithmetic Circuits Based on Deterministic Bit-Streams. - Kamyar Givaki; Ahmad Khonsari; MohammadHosein Gholamrezaei; Saeid Gorgin 001; M. Hassan Najafi (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)