12 citations · 20 across the 13 of their papers we have counts for
13 papers
Compressing Deep Neural Networks Using Explainable AI
Kimia Soroush, Mohsen Raji, Behnam Ghavami
Deep neural networks (DNNs) have demonstrated remarkable performance in many tasks but it often comes at a high computational cost and memory usage. Compression techniques, such as…
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…
scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation
Ebrahim Farahmand, Mohammad Javad Askarizadeh, Ali Mahani +4
In this paper, we propose a scalable approximate multiplier design, scaleTRIM, that approximates the multiplication operation using fitted linear functions, also referred to as lin…
BDFA: A Blind Data Adversarial Bit-flip Attack on Deep Neural Networks
Behnam Ghavami, Mani Sadati, Mohammad Shahidzadeh +2
Adversarial bit-flip attack (BFA) on Neural Network weights can result in catastrophic accuracy degradation by flipping a very small number of bits. A major drawback of prior bit f…
FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions
Behnam Ghavami, Mani Sadati, Zhenman Fang +1
Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars. In such DNN-based systems, error r…
Stealthy Attack on Algorithmic-Protected DNNs via Smart Bit Flipping
Behnam Ghavami, Seyd Movi, Zhenman Fang +1
Recently, deep neural networks (DNNs) have been deployed in safety-critical systems such as autonomous vehicles and medical devices. Shortly after that, the vulnerability of DNNs w…
SeaPlace: Process Variation Aware Placement for Reliable Combinational Circuits against SETs and METs
Kiarash Saremi, Hossein Pedram, Behnam Ghavami +3
Nowadays nanoscale combinational circuits are facing significant reliability challenges including soft errors and process variations. This paper presents novel process variation-aw…
An Efficient Communication Protocol for FPGA IP Protection
Farzane Khajuyi, Behnam Ghavami, Human Nikmehr
We introduce a protection-based IP security scheme to protect soft and firm IP cores which are used on FPGA devices. The scheme is based on Finite State Machin (FSM) obfuscation an…