5 papers
From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks
Sepideh Kheirollahi, Mohammad Rasoul Roshanshah
Electroencephalogram (EEG) analysis remains the clinical gold standard for epilepsy diagnosis and seizure detection. While Deep Learning (DL) has significantly advanced automated E…
ReSCom: A Reconfigurable Spiking Neural Network Accelerator Using Stochastic Computing
Ali Alipour Fereidani, Mohammad Rasoul Roshanshah, Saeed Safari
Spiking Neural Networks (SNNs) provide an attractive framework for energy-efficient inference due to their event-driven computation and biologically inspired dynamics. However, eff…
SupraSNN: Exploiting Synapse-Level Parallelism in Spiking Neural Network Accelerators through Co-Optimized Mapping and Scheduling
Seyed Sadra Ghavami, Mohammad Hossein Nikkhah, Mohammad Rasoul Roshanshah +1
Spiking Neural Networks (SNNs) offer a brain-inspired path toward highly efficient computation, but their practical deployment is constrained by the challenge of managing and execu…
Flexi-NeurA: A Flexible Neuromorphic Accelerator with Adaptive Bit-Precision Exploration for Edge Devices
Mohammad Farahani, Mohammad Rasoul Roshanshah, Saeed Safari
Neuromorphic accelerators promise unparalleled energy efficiency and computational density for spiking neural networks, especially in wearable biomedical devices and neural prosthe…
Quantum-Efficient Convolution through Sparse Matrix Encoding and Low-Depth Inner Product Circuits
Mohammad Rasoul Roshanshah, Payman Kazemikhah, Hossein Aghababa
Convolution operations are foundational to classical image processing and modern deep learning architectures, yet their extension into the quantum domain has remained algorithmical…