Description

Since the rise of deep learning in 2012, much progress has been made in deep-learning-based AI tasks such as image/video understanding and natural language understanding, as well as GPU/accelerator architectures that greatly improve the training and inference speed for neural-network models. As the industry players race to develop ambitious applications such as self-driving vehicles, cashier-less supermarkets, human-level interactive robot systems, and human intelligence augmentation, major research challenges remain in computational methods as well as hardware/software infrastructures required for these applications to be effective, robust, responsive, accountable and cost-effective. Innovations in scalable iterative solvers and graph algorithms will be needed to achieve these application-level goals but will also impose much higher-level of data storage capacity, access latency, energy efficiency, and processing throughput. In this talk, Wen-mei Hwu presents on recent progress in building highly performant AI task libraries, creating full AI applications, providing AI application development tools, and prototyping the Erudite system at the IBM-Illinois C3SR.