Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Winograd is generally utilized to optimize convolution performance and computational
efficiency because of the reduced multiplication operations, but the reliability issues brought …

Transformers' compute-intensive operations pose enormous challenges for their deployment
in resource-constrained EdgeAI/tiny ML devices. As an established neural network …

Machine learning (ML) is widely used today, especially through deep neural networks
(DNNs); however, increasing computational load and resource requirements have led to …

Robotic technologies have been an indispensable part for improving human productivity
since they have been helping humans in completing diverse, complex, and intensive tasks …

Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network
(DNN) architectures for a given application under given system constraints. DNNs are …

Recently, Deep Neural Networks (DNNs) have achieved remarkable performances in many
applications, while several studies have enhanced their vulnerabilities to malicious attacks …

Effective design space exploration (DSE) is paramount for hardware/software codesigns of
deep learning accelerators that must meet strict execution constraints. For their vast search …

Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships
between multiple objects for image classification tasks. Other than achieving high accuracy …

Recent studies have shown that Machine Learning (ML) algorithm suffers from several
vulnerability threats. Among them, adversarial attacks represent one of the most critical …