The transformer-based model has great potential to deliver higher accuracy for object
recognition applications when comparing it with the convolution neural network (CNN). Yet …

In the scenarios of the Internet of Things, fall detection holds increasing significance in the
health monitoring of elderly individuals. While most current research has achieved …

In a variety of computer vision applications, convolutional neural networks (CNNs) have
achieved excellent accuracy. However, in order for a CNN to operate on embedded …

Deep neural networks (DNNs) have demonstrated their effectiveness in a wide range of
computer vision tasks, with the state-of-the-art results obtained through complex and deep …

To fulfil the tight area and memory constraints in IoT applications, the design of efficient
Convolutional Neural Network (CNN) hardware becomes crucial. Quantization of CNN is …

Convolutional Neural Network (CNN) plays an important role in several machine learning
tasks related to speech, image, and video processing applications. The increasing demand …

This paper presents the architecture of a Convolution Neural Network (CNN) accelerator
based on a new processing element (PE) array called a diagonal cyclic array (DCA). As …

This paper proposes a computation-array-centered dataflow, which adjusts the convolution
with different kernel sizes to a unified computing manner and reduces the dimension of …

As deep neural network (DNN) models become more accurate, problems such as large
model parameters and high computational complexity have become increasingly prominent …

Post-training quantization (PTQ) has been proven an efficient model compression technique
for Convolution Neural Networks (CNNs), without re-training or access to labeled datasets …