Deep Learning and Machine Intelligence is maturing to the point where is it is being deployed to many applications, particularly large data, imaging classification and detection. This talk addresses the challenges of deep learning from a computational challenge perspective and discusses the ways in which new compute platforms of Zen (x86) and Vega (GPU) provide high performance solutions to different training and inference applications. The ROCm software stack completes the support with libraries and framework support for a variety of environments.
ABOUT THE COLLOQUIUM:
See the Colloquium website, http://ee380.stanford.edu, for scheduled speakers, FAQ, and additional information. Stanford and SCPD students can enroll in EE380 for one unit of credit. Anyone is welcome to attend; talks are webcast live and archived for on-demand viewing over the web.
Allen Rush is a fellow at AMD, focusing on imaging and machine learning architecture development. He has been active in imaging and computer vision projects for over 25 years, including several startups. He is the domain architect for ISP and current machine learning development activities in HW, SW and application support.