3 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
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…
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…