The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in
many scenarios, including safety-critical applications such as autonomous driving. In this …

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 …

Multiplication is the most resource-hungry operation in the neural network's processing
elements. In this paper, we propose an architecture of a novel adaptive fault-tolerant …

Systolic array has emerged as a prominent archi-tecture for Deep Neural Network (DNN)
hardware accelerators, providing high-throughput and low-latency performance essen-tial …

Deep Neural Networks (DNNs) and their accelerators are being deployed ever more
frequently in safety-critical applications leading to increasing reliability concerns. A …

Reliability assessment is mandatory to guarantee the correct behavior of Deep Neural
Network (DNN) hardware accelerators in safety-critical applications. While fault injection …

Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical
applications raises new reliability concerns. In practice, methods for fault injection by …

Abstract Machine Learning (ML) is currently being exploited in numerous applications, being
one of the most effective Artificial Intelligence (AI) technologies used in diverse fields, such …

Deep Neural Network (DNN) hardware accelerators are essential in a spectrum of safety-
critical edge-AI applications with stringent reliability, energy efficiency, and latency …