Our partners: Professor Alvin Lebeck (Duke University, USA), Associate Professor Michael Taylor (University of Washington, USA), Dr Xin Fu (University of Houston, USA), Dr Lizy John (University of Texas at Austin, USA)
Industry partners: Sara Hooker (Google Brain), Mike Zhan (Ambarella Inc), Yongchao Liu (Alibaba Research)
AI-driven system design has become prevalent, from embedded systems such as IoT and edge computing to large-scale data centre and HPC system design.
However, the current data-flow driven design has shown significant inefficiency on new deep learning networks.
By leveraging our unique research capability via looking into different design stacks from programming language, compiler and runtime to hardware customisation, we’re exploring other better alternatives (such as memory- and data-centric designs) to help practitioners build their software and hardware layers of the desired deep learning systems.
Our primary focus is to provide general strategies for designing accelerators or systems that can accommodate the unique aspects of emerging DL and ML networks.
We’re also exploring design principles and optimisation strategies for the emerging probabilistic machine learning models such as Bayesian Neural Networks and of Markov Chain Monte Carlo.
Our recent works have resulted in top HPC and architecture conferences including Supercomputing, ISCA and HPCA.