The main goal of the project is to test Intel"s oneAPI as a framework to speed up the 3DGAN training processes across multiple hardware architectures. The work consists in analyzing the behavior of 3DGAN under Intel oneAPI by using the Intel AI Analytics Toolkit. This toolkit provides Python frameworks and tools to accelerate end-to-end data science and analytics pipelines on Intel architectures. It uses oneAPI libraries for low-level compute optimizations, thus, maximizing performance from preprocessing through machine learning. As a result, oneAPI made a difference, the model converged faster
In this paper, we analyze heterogeneous performance exhibited by some popular deep learning software...
The widely-adopted practice is to train deep learning models with specialized hardware accelerators,...
Neural networks are becoming more and more popular in scientific field and in the industry. It is mo...
AI and deep learning are experiencing explosive growth in almost every domain involving analysis of ...
A recent effort to explore a neural network inference in FPGAs focusing on low-latency applications ...
Detailed simulation is one of the most expensive tasks, in terms of time and computing resources for...
In recent years, the continuous development of artificial intelligence has largely been driven by al...
Deep learning is widely used in many problem areas, namely computer vision, natural language process...
With the rapid growth of deep learning models and higher expectations for their accuracy and through...
The deep learning community focuses on training networks for a better accuracy on GPU servers. Howev...
Heterogeneous computing systems provide high performance and energy efficiency. However, to optimall...
PU is a powerful, pervasive, and indispensable platform for running deep learning (DL) workloads in ...
In recent years, artificial intelligence (AI) became a key enabling technology for many domains. To ...
peer reviewedWith renewed global interest for Artificial Intelligence (AI) methods, the past decade ...
While machine learning (ML) has been widely used in real-life applications, the complex nature of re...
In this paper, we analyze heterogeneous performance exhibited by some popular deep learning software...
The widely-adopted practice is to train deep learning models with specialized hardware accelerators,...
Neural networks are becoming more and more popular in scientific field and in the industry. It is mo...
AI and deep learning are experiencing explosive growth in almost every domain involving analysis of ...
A recent effort to explore a neural network inference in FPGAs focusing on low-latency applications ...
Detailed simulation is one of the most expensive tasks, in terms of time and computing resources for...
In recent years, the continuous development of artificial intelligence has largely been driven by al...
Deep learning is widely used in many problem areas, namely computer vision, natural language process...
With the rapid growth of deep learning models and higher expectations for their accuracy and through...
The deep learning community focuses on training networks for a better accuracy on GPU servers. Howev...
Heterogeneous computing systems provide high performance and energy efficiency. However, to optimall...
PU is a powerful, pervasive, and indispensable platform for running deep learning (DL) workloads in ...
In recent years, artificial intelligence (AI) became a key enabling technology for many domains. To ...
peer reviewedWith renewed global interest for Artificial Intelligence (AI) methods, the past decade ...
While machine learning (ML) has been widely used in real-life applications, the complex nature of re...
In this paper, we analyze heterogeneous performance exhibited by some popular deep learning software...
The widely-adopted practice is to train deep learning models with specialized hardware accelerators,...
Neural networks are becoming more and more popular in scientific field and in the industry. It is mo...