10 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…
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…
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…
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…
Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning
Mohammad Hasan Ahmadilivani, Seyedhamidreza Mousavi, Jaan Raik +2
Convolutional Neural Networks (CNNs) have become integral in safety-critical applications, thus raising concerns about their fault tolerance. Conventional hardware-dependent fault…
SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
Mahdi Taheri, Masoud Daneshtalab, Jaan Raik +5
Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for depl…
AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators
Mahdi Taheri, Natalia Cherezova, Samira Nazari +6
In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators
Mahdi Taheri, Natalia Cherezova, Mohammad Saeed Ansari +4
The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability stand along with the need for reducing the computational burden on the hardware platforms,…