An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks
Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial
Intelligence (AI) applications. Their ability to go beyond human precision has made these …
Intelligence (AI) applications. Their ability to go beyond human precision has made these …
Pervasive AI for IoT applications: A survey on resource-efficient distributed artificial intelligence
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …
Things (IoT) applications and services, spanning from recommendation systems and speech …
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 …
Distributed artificial intelligence-as-a-service (DAIaaS) for smarter IoE and 6G environments
Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything
we do, even in finding our “true love” and the “significant other”. While 5G promises us high …
we do, even in finding our “true love” and the “significant other”. While 5G promises us high …
Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks
Artificial neural networks have demonstrated superiority over traditional computing
architectures in tasks such as pattern classification and learning. However, they do not …
architectures in tasks such as pattern classification and learning. However, they do not …
An efficient spiking neural network for recognizing gestures with a dvs camera on the loihi neuromorphic processor
R Massa, A Marchisio, M Martina… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs), the third generation NNs, have come under the spotlight
for machine learning based applications due to their biological plausibility and reduced …
for machine learning based applications due to their biological plausibility and reduced …
Accelerating neural network inference on FPGA-based platforms—A survey
R Wu, X Guo, J Du, J Li - Electronics, 2021 - mdpi.com
The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …
such as object identification, image/video recognition and semantic segmentation. Neural …
Design possibilities and challenges of DNN models: a review on the perspective of end devices
Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …
resource-constrained devices have become abundant in recent years. As of now, the …
Energy-efficient inference on the edge exploiting TinyML capabilities for UAVs
In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased
dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost …
dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost …
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 …