This paper present contributions to the state-of-the art for graphics processing unit (GPU-
based) embedded intelligence (EI) research for architectures and applications. This paper …

Implementing artificial intelligence (AI) in the Internet of Things (IoT) involves a move from
the cloud to the heterogeneous and low-power edge, following an urgent demand for …

To achieve high accuracy when performing deep learning, it is necessary to use a large-
scale training model. However, due to the limitations of GPU memory, it is difficult to train …

Due to its fantastic performance in the quality of the images created, Generator Adversarial
Networks have recently become a viable option for image reconstruction. The main problem …

The exponential growth of the training dataset and the size of the large language model
(LLM) significantly outpaces the incremental memory capacity increase in the graphics pro …

Modern deep neural networks cannot be often trained on a single GPU due to large model
size and large data size. Model parallelism splits a model for multiple GPUs, but making it …

Deep Learning frameworks, such as TensorFlow, MXNet, Chainer, provide many basic
building blocks for designing effective neural network models for various applications (eg …

The increasing adoption of rooftop photovoltaic (PV) power generation systems in
residential areas necessitates accurate monitoring and disaggregation of behind-the-meter …

MRLD™ is a new type of reconfigurable device constructed by general SRAM array
(multiple-LUTs) which has the advantages including small delay, low power and low …

To accommodate lots of training data and complex training models,“distributed” deep
learning training has become employed more and more frequently. However …