Hardware approximate techniques for deep neural network accelerators: A survey
Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …
Secure and trustworthy artificial intelligence-extended reality (AI-XR) for metaverses
Metaverse is expected to emerge as a new paradigm for the next-generation Internet,
providing fully immersive and personalized experiences to socialize, work, and play in self …
providing fully immersive and personalized experiences to socialize, work, and play in self …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Deep learning for edge computing: Current trends, cross-layer optimizations, and open research challenges
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …
their unmatchable performance in several applications, such as image processing, computer …
[HTML][HTML] Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study
The use of artificial intelligence (AI) at the edge is transforming every aspect of the lives of
human beings from scheduling daily activities to personalized shopping recommendations …
human beings from scheduling daily activities to personalized shopping recommendations …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
All your fake detector are belong to us: evaluating adversarial robustness of fake-news detectors under black-box settings
With the hyperconnectivity and ubiquity of the Internet, the fake news problem now presents
a greater threat than ever before. One promising solution for countering this threat is to …
a greater threat than ever before. One promising solution for countering this threat is to …
Securing deep spiking neural networks against adversarial attacks through inherent structural parameters
R El-Allami, A Marchisio, M Shafique… - … Design, Automation & …, 2021 - ieeexplore.ieee.org
Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …
Evasion Attack and Defense On Machine Learning Models in Cyber-Physical Systems: A Survey
Cyber-physical systems (CPS) are increasingly relying on machine learning (ML)
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
APNAS: Accuracy-and-performance-aware neural architecture search for neural hardware accelerators
Designing resource-efficient deep neural networks (DNNs) is a challenging task due to the
enormous diversity of applications as well as their time-consuming design, training …
enormous diversity of applications as well as their time-consuming design, training …