12 citations · 20 across the 13 of their papers we have counts for
3 papers · 1 filter
ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters
Behnam Ghavami, Mohammad Shahidzadeh, Lesley Shannon +1
Low-precision weights and activations in deep neural networks (DNNs) outperform their full-precision counterparts in terms of hardware efficiency. When implemented with low-precisi…
Quantizing YOLOv7: A Comprehensive Study
Mohammadamin Baghbanbashi, Mohsen Raji, Behnam Ghavami
YOLO is a deep neural network (DNN) model presented for robust real-time object detection following the one-stage inference approach. It outperforms other real-time object detector…
DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization
Behnam Ghavami, Amin Kamjoo, Lesley Shannon +1
The imperative to deploy Deep Neural Network (DNN) models on resource-constrained edge devices, spurred by privacy concerns, has become increasingly apparent. To facilitate the tra…