The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

Field Programmable Gate Array (FPGA) has become an efficient accelerator for
convolutional neural network (CNN) inference due to its high performance and flexibility. To …

Convolutional Neural Networks (CNN) continue to dominate research in the area of
hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its …

The sustainable cultivation of organic vegetables and the associated problem of weed
control has been a current research topic for some time. Despite this, the use of chemical …

Convolutional neural networks (CNNs) are used for classification, as they can extract
complex features from input data. The training and inference of these networks typically …

To realize deep learning techniques, a type of deep neural network (DNN) called a
convolutional neural networks (CNN) is among the most widely used models aimed at …

This paper shows the possibility of the existing low power register transfer level (RTL)
techniques can be effective as a low power design scheme for CNN-based object …

This study proposes a new method for diagnosing Parkinson's disease using audio signals
and FPGA-based convolutional neural networks. The proposed method involves training a …

Grout is an important part of the bolt anchorage system; once having the damage, the
cohesive force will not reach the requirement, which may affect the support effect. Moreover …

On-chip memory is one of the core components of deep learning accelerators. In general,
the area used by the on-chip memory accounts for around 30% of the total chip area. With …