insideHPC

Scalable and Distributed DNN Training on Modern HPC Systems

In this video from the Swiss HPC Conference, DK Panda from Ohio State University presents: Scalable and Distributed DNN Training on Modern HPC Systems. The current wave of advances in Deep Learning ...
Concordia University

INVITED SPEAKER SEMINAR - Decentralized Cloud/Edge DNN Architectures

The proposed framework addresses the challenges of handling diverse training data distributions and the limitations of individual DNN agent learning abilities. The LENC framework ensures the ...
Science Daily

Early Bird uses 10 times less energy to train deep neural networks

"This means you can train a DNN to achieve the same or even better accuracy for a given task in about 10% or less of the time needed for traditional training, which can lead to more than one order ...
Semiconductor Engineering

Low-Power Heterogeneous Compute Cluster For TinyML DNN Inference And On-Chip Training

A new technical paper titled “DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training” was published by researchers at University of Bologna and ETH Zurich ...
TechNode

Briefing: Chinese researchers propose new, more efficient DNN training method

51001614 - robotic hand, accessing on laptop, the virtual world of information. concept of artificial intelligence and replacement of humans by machines. Every Wednesday and Friday, TechNode’s ...
InfoQ

Xipeng Shen on a New Technique to Reduce Deep-Learning Training Time

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
insideHPC

How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems

In this video from the Stanford HPC Conference, DK Panda from Ohio State University presents: How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems. This talk ...
EurekAlert!

Early Bird uses 10 times less energy to train deep neural networks

HOUSTON -- (May 18, 2020) -- Rice University's Early Bird could care less about the worm; it's looking for megatons of greenhouse gas emissions. Early Bird is an energy-efficient method for training ...