3 papers
cs.AR2021
Phantom: A High-Performance Computational Core for Sparse Convolutional Neural Networks
Mahmood Azhar Qureshi, Arslan Munir
Sparse convolutional neural networks (CNNs) have gained significant traction over the past few years as sparse CNNs can drastically decrease the model size and computations, if exp…
cs.CR2020
PUF-RLA: A PUF-based Reliable and Lightweight Authentication Protocol employing Binary String Shuffling
Mahmood Azhar Qureshi, Arslan Munir
Physically unclonable functions (PUFs) can be employed for device identification, authentication, secret key storage, and other security tasks. However, PUFs are susceptible to mod…
cs.AR2020
NeuroMAX: A High Throughput, Multi-Threaded, Log-Based Accelerator for Convolutional Neural Networks
Mahmood Azhar Qureshi, Arslan Munir
Convolutional neural networks (CNNs) require high throughput hardware accelerators for real time applications owing to their huge computational cost. Most traditional CNN accelerat…