A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead
K Zaman, A Marchisio, MA Hanif… - arXiv preprint arXiv …, 2023 - arxiv.org
Quantum Computing (QC) claims to improve the efficiency of solving complex problems,
compared to classical computing. When QC is applied to Machine Learning (ML) …
compared to classical computing. When QC is applied to Machine Learning (ML) …
Low latency deep learning inference model for distributed intelligent IoT edge clusters
Edge computing is a new paradigm enabling intelligent applications for the Internet of
Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the …
Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the …
NASCaps: A framework for neural architecture search to optimize the accuracy and hardware efficiency of convolutional capsule networks
Deep Neural Networks (DNNs) have made significant improvements to reach the desired
accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently …
accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently …
Performance evaluation of deep learning compilers for edge inference
Recently, edge computing has received considerable attention as a promising means to
provide Deep Learning (DL) based services. However, due to the limited computation …
provide Deep Learning (DL) based services. However, due to the limited computation …
Dvs-attacks: Adversarial attacks on dynamic vision sensors for spiking neural networks
A Marchisio, G Pira, M Martina… - … Joint Conference on …, 2021 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …
CRACAU: Byzantine machine learning meets industrial edge computing in industry 5.0
Industry 5.0 is emerging as a result of the advancement in networking and communication
technologies, artificial intelligence, distributed computing, and beyond 5G. Among the …
technologies, artificial intelligence, distributed computing, and beyond 5G. Among the …
Spiker: an fpga-optimized hardware accelerator for spiking neural networks
A Carpegna, A Savino… - 2022 IEEE Computer …, 2022 - ieeexplore.ieee.org
Spiking Neural Networks (SNN) are an emerging type of biologically plausible and efficient
Artificial Neural Network (ANN). This work presents the development of a hardware …
Artificial Neural Network (ANN). This work presents the development of a hardware …
Automated HW/SW co-design for edge AI: State, challenges and steps ahead
O Bringmann, W Ecker, I Feldner… - Proceedings of the …, 2021 - dl.acm.org
Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …
SwiftTron: An efficient hardware accelerator for quantized transformers
A Marchisio, D Dura, M Capra… - … Joint Conference on …, 2023 - ieeexplore.ieee.org
Transformers' compute-intensive operations pose enormous challenges for their deployment
in resource-constrained EdgeAI/tiny ML devices. As an established neural network …
in resource-constrained EdgeAI/tiny ML devices. As an established neural network …
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 …