In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

The depthwise separable convolution is commonly seen in convolutional neural networks
(CNNs), and is widely used to reduce the computation overhead of a standard multi-channel …

The resurgence of artificial intelligence enabled by deep learning and high performance
computing has seen a dramatic increase of demand in the accuracy of deep learning model …

Autonomous systems execute complex tasks to perceive the environment and take self-
aware decisions with limited human interaction. This autonomy is commonly achieved with …

In the machine learning era, model inference efficiency is one of the most important issues
for machine learning systems. It is a major challenge to find the optimal configuration in a …

Reducing the number of data accesses in memory hierarchy is of paramount importance on
modern computer systems. One of the key optimizations addressing this problem is loop …

To investigate wavefield depth extrapolation using the full-wave equation, we have derived
a new depth extrapolation scheme for migration using functions of the vertical wavenumber …

This study examined the relative performance of Deep Reinforcement Learning compared to
a neuroevolution algorithm called NEAT when used to train AIs in a discrete game …

High performance implementation of matrix multiplication is essential for scientific
computing. The memory access procedure is quite possible to be the bottleneck of matrix …

General Matrix Multiplication (GEMM) is a key subprogram in high-performance computing
(HPC) and deep learning workloads. With the rising significance of power and energy …