Software-Hardware Co-Design for Future AI-driven Complex Systems

Speaker:  Shuaiwen Leon Song – Baltimore, MD, United States
Topic(s):  Architecture, Embedded Systems and Electronics, Robotics


AI-driven system design has become prevalent, from embedded systems (e.g., IoT and edge computing) to large-scale data center and HPC system design. However, the current data-flow driven design has shown significant inefficiency on new deep learning network designs. In this session, I will discuss how to use memory- and data-centric design to help practitioners build their software and hardware layers of the desired deep learning system architectures. I will demonstrate usage cases from some of the emerging networks, including large LSTM training, Bayesian Neural Network Inference and Training, CapsuleNet inference, etc.

About this Lecture

Number of Slides:  45
Duration:  50 minutes
Languages Available:  English
Last Updated: 

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