Despite recent improvements, the computing capability of Edge Computing devices is still
inferior to high-end servers, so special methodologies are required to consider the …

The powerful parallel computing capability of GPU and the development of matrix
processing unit in recent years provide more possibilities to improve the performance of …

The tremendous success of convolutional neural network (CNN) has made it ubiquitous in
many fields of human endeavor. Many applications such as biomedical analysis and …

Model sparsity is a promising approach to reducing parameters or FLOPs of convolutional
neural networks (CNNs). Compared to unstructured or coarse-grained structured sparsity …

Recommendation systems are essential to the operation of the majority of internet services,
with Deep Learning Recommendation Models (DLRMs) serving as a crucial component …

The emergence of High-Performance Computing (HPC) and Artificial Intelligence (AI) has
significantly expanded the applications of three-dimensional convolutional neural networks …

In recent years, phoniatric diagnostics has seen a surge of interest in structured light-based
high-speed video endoscopy, as it enables the observation of oscillating human vocal folds …

The development of deep learning recommendation models (DLRM) and recommendation
systems has significantly improved the precision of information matching. Due to distinct …

With the widespread application of deep learning, represented by convolutional neural
networks (CNNs), the computational requirements of neural network models have increased …

随着以卷积神经网络为代表的深度学**得到广泛应用, 神经网络模型中的计算量也急速增长,
推动了深度学**加速器的发展. 如何针对加速器硬件的体系结构特性进行加速和优化神经网络 …