Deep Learning Infrastructure Challenges. There are three significant obstacles for you to be aware of when designing a deep learning infrastructure: scalability, customizing for each workload, and ...

The work marks a beginning in using machine learning techniques to optimize the architecture of chips. Written by Tiernan Ray, Senior Contributing Writer Feb. 28, 2021 at 2:05 p.m. PT ...

This is especially true with the addition to deep learning for medical images into more hospital settings—something that adds more hardware and software heft to an already top-heavy stack. Earlier ...

Get the details of the unique rack-scale architecture provided by NVIDIA and NetApp. Find out how the purpose-built infrastructure allows organizations to deploy deep-learning workloads and handle ...

As a result, they continue to miss unknown threats, and incur high infrastructure costs. That’s the worst of both worlds for an enterprise,” said Guy Caspi, CEO and Co-Founder of Deep Instinct.

Amini (pictured) leads an interdisciplinary team of researchers from the College of Engineering and Computing that has received a five-year $1 million grant from the U.S. Department of Homeland ...

The graph below shows the total number of publications each year in Automated Crack Detection in Civil Infrastructure Using Deep Learning. References [1] Detection of cracks in concrete using near ...

MicroCloud Hologram Inc. Develops a Noise-Resistant Deep Quantum Neural Network (DQNN) Architecture to Optimize Training Efficiency for Quantum Learning Tasks Provided by GlobeNewswire Jun 10 ...

MicroCloud Hologram Inc. announces a noise-resistant Deep Quantum Neural Network architecture, advancing quantum computing and machine learning efficiency. Quiver AI Summary ...