This paper proposes field-programmable gate array (FPGA) acceleration on a scalable multi-
layer perceptron (MLP) neural network for classifying handwritten digits. First, an …

This proposed research explores a novel approach to image classification by deploying a
complex-valued neural network (CVNN) on a Field-Programmable Gate Array (FPGA) …

Due to the high parallelism and efficiency, Convolution acceleration and buffer pipeline
architecture are common solutions for convolutional neural network (CNN) acceleration …

Due to the high parallelism, Data flow architecture is a common solution for deep neural
network (DNN) acceleration, however, existing DNN accelerate solutions exhibit limited …

This paper presents a parameterizable design generator on convolutional neural networks
(CNNs) using the Chisel hardware construction language (HCL). By parameterizing …

Convolutional Neural Networks (CNNs) are a very popular class of artificial neural networks.
Current CNN models provide remarkable performance and accuracy in image processing …

In this study, we introduced a mixed-precision weights network (MPWN), which is a
quantization neural network that jointly utilizes three different weight spaces: binary {− 1, 1} …

Deep neural networks (DNNs) are prevalent for many applications related to classification,
prediction and regression. To perform different applications with better performance and …

Recent works aimed at expanding the target offloading capabilities of OpenMP to FPGA
platforms. While enabling the easy construction of heterogeneous systems, the approach …

This paper presents a scalable design architecture for lightweight neural networks for
resource-limited devices. As case studies, several field-programmable gate array (FPGA) …