12 citations · 18 across the 9 of their papers we have counts for
9 papers
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…
A Cycle-Accurate Soft Error Vulnerability Analysis Framework for FPGA-based Designs
Eduardo Rhod, Behnam Ghavami, Zhenman Fang +1
Many aerospace and automotive applications use FPGAs in their designs due to their low power and reconfigurability requirements. Meanwhile, such applications also pose a high stand…
A Decision Making Approach for Chemotherapy Planning based on Evolutionary Processing
Mina Jafari, Behnam Ghavami, Vahid Sattari Naeini
The problem of chemotherapy treatment optimization can be defined in order to minimize the size of the tumor without endangering the patient's health; therefore, chemotherapy requi…
Unraveling the Integration of Deep Machine Learning in FPGA CAD Flow: A Concise Survey and Future Insights
Behnam Ghavami, Lesley Shannon
This paper presents an overview of the integration of deep machine learning (DL) in FPGA CAD design flow, focusing on high-level and logic synthesis, placement, and routing. Our an…