7 papers
Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices
Farough Shayeste Roodi, Parham Zilouchian Moghaddam, Mahdi Mohammadi-nasab +3
Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector. Many automated healthcare services rely on two crucial biological signals, namely ECG and EEG,…
Contextual Range-View Projection for 3D LiDAR Point Clouds
Seyedali Mousavi, Seyedhamidreza Mousavi, Masoud Daneshtalab
Range-view projection provides an efficient method for transforming 3D LiDAR point clouds into 2D range image representations, enabling effective processing with 2D deep learning m…
DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network
Mohammad Hasan Ahmadilivani, Jaan Raik, Masoud Daneshtalab +1
The growing exploitation of Machine Learning (ML) in safety-critical applications necessitates rigorous safety analysis. Hardware reliability assessment is a major concern with res…
AxLLM: accelerator architecture for large language models with computation reuse capability
Soroush Ahadi, Mehdi Modarressi, Masoud Daneshtalab
Large language models demand massive computational power and memory resources, posing significant challenges for efficient deployment. While quantization has been widely explored t…
ProARD: progressive adversarial robustness distillation: provide wide range of robust students
Seyedhamidreza Mousavi, Seyedali Mousavi, Masoud Daneshtalab
Adversarial Robustness Distillation (ARD) has emerged as an effective method to enhance the robustness of lightweight deep neural networks against adversarial attacks. Current ARD…
ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs
Seyedhamidreza Mousavi, Mohammad Hasan Ahmadilivani, Jaan Raik +2
Deep Neural Networks (DNNs) are extensively employed in safety-critical applications where ensuring hardware reliability is a primary concern. To enhance the reliability of DNNs ag…