AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators Page 1 ©
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As the deployment of artificial intelligence (AI) algorithms at edge devices becomes
increasingly prevalent, enhancing the robustness and reliability of autonomous AI-based …

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

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 …

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

SRAM field-programmable gate arrays (FPGAs) are popular computing platforms for
implementing neural networks (NNs) due to their flexibility and low recurring engineering …

Very recently, Artificial Intelligence started undergoing a remarkable transformation by
moving closer to the source of data, thus establishing the Edge AI concept. This trend sets …

Since Deep Neural Networks (DNNs) gracefully withstands approximation due to its inherent
redundancy, Approximate Computing (AxC) can be applied to reduce power consumption …

Machine learning-based embedded systems employed in safety-critical applications such as
aerospace and autonomous driving need to be robust against perturbations produced by …

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