Liu Liu MAE

Date: 
Tuesday, May 14, 2019 - 3:00pm to 5:00pm
Location: 
HFH 1132
Title: 
Efficient Methods and Hardware Specialization for Deep Learning
Speaker: 
Liu Liu
Committee: 
Yuan Xie, Yufei Ding, Tim Sherwood, William Wang

Abstract:

Deep Neural Networks (DNNs) have led to significant breakthroughs that expand the possibilities of applying artificial intelligence (AI) to many domains. Although DNNs have been driving the mainstream AI applications, it is becoming challenging to deploy them efficiently on modern hardware due to their increasingly compute-intensive and data-intensive nature.

In this talk, I will review the recent hardware-centric advancements in domain-specific architectures for DNNs. Then, I will discuss the representative software-centric techniques, such as quantization and pruning, that improve execution efficiency. Finally, I will present my research plan on integrating software methods and hardware specialization for efficient deep learning systems at the post-Moore’s law era.

 

Everyone welcome!

Everyone welcome!