A survey and taxonomy of FPGA-based deep learning accelerators
Deep learning, the fastest growing segment of Artificial Neural Network (ANN), has led to the
emergence of many machine learning applications and their implementation across multiple …
emergence of many machine learning applications and their implementation across multiple …
Sparse reram engine: Joint exploration of activation and weight sparsity in compressed neural networks
Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to
achieve energy efficiency for DNN inference accelerators. However, due to the tightly …
achieve energy efficiency for DNN inference accelerators. However, due to the tightly …
COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble
The COVID-19 pandemic has collapsed the public healthcare systems, along with severely
damaging the economy of the world. The SARS-CoV-2 virus also known as the coronavirus …
damaging the economy of the world. The SARS-CoV-2 virus also known as the coronavirus …
Adaptivenet: Post-deployment neural architecture adaptation for diverse edge environments
Deep learning models are increasingly deployed to edge devices for real-time applications.
To ensure stable service quality across diverse edge environments, it is highly desirable to …
To ensure stable service quality across diverse edge environments, it is highly desirable to …
On the reduction of computational complexity of deep convolutional neural networks
Deep convolutional neural networks (ConvNets), which are at the heart of many new
emerging applications, achieve remarkable performance in audio and visual recognition …
emerging applications, achieve remarkable performance in audio and visual recognition …
Map** neural networks to FPGA-based IoT devices for ultra-low latency processing
M Wielgosz, M Karwatowski - Sensors, 2019 - mdpi.com
Internet of things (IoT) infrastructure, fast access to knowledge becomes critical. In some
application domains, such as robotics, autonomous driving, predictive maintenance, and …
application domains, such as robotics, autonomous driving, predictive maintenance, and …
A method for medical data analysis using the LogNNet for clinical decision support systems and edge computing in healthcare
A Velichko - Sensors, 2021 - mdpi.com
Edge computing is a fast-growing and much needed technology in healthcare. The problem
of implementing artificial intelligence on edge devices is the complexity and high resource …
of implementing artificial intelligence on edge devices is the complexity and high resource …
An efficient fpga-based depthwise separable convolutional neural network accelerator with hardware pruning
Z Liu, Q Liu, S Yan, RCC Cheung - ACM Transactions on Reconfigurable …, 2024 - dl.acm.org
Convolutional neural networks (CNNs) have been widely deployed in computer vision tasks.
However, the computation and resource intensive characteristics of CNN bring obstacles to …
However, the computation and resource intensive characteristics of CNN bring obstacles to …
EtinyNet: Extremely tiny network for tinyML
K Xu, Y Li, H Zhang, R Lai, L Gu - … of the AAAI conference on artificial …, 2022 - ojs.aaai.org
There are many AI applications in high-income countries because their implementation
depends on expensive GPU cards (~ 2000$) and reliable power supply (~ 200W). To deploy …
depends on expensive GPU cards (~ 2000$) and reliable power supply (~ 200W). To deploy …
Bbnet: a novel convolutional neural network structure in edge-cloud collaborative inference
H Zhou, W Zhang, C Wang, X Ma, H Yu - Sensors, 2021 - mdpi.com
Edge-cloud collaborative inference can significantly reduce the delay of a deep neural
network (DNN) by dividing the network between mobile edge and cloud. However, the in …
network (DNN) by dividing the network between mobile edge and cloud. However, the in …