Artificial Intelligence (AI) is bringing significant benefits to human society with outstanding potential to address essential human concerns in the future. Deep neural networks have shown significant improvements in many AI applications. However, the benefit comes at the high cost of computational resources and engineer resources. For mobile devices with a tight power budget, such high demands for computation become prohibitive. On the other side, there is a shortage of experts who can design neural networks, which are notoriously hard to tune. This project systematically investigates efficient neural network architectures and their hardware accelerators to make them run fast at low power. It also aims to accelerate the design cycle by AI-based design automation (AI-designed AI). Such design methodology can support machine learning research and education,, while significantly improving the productivity of machine learning models. People no longer have to hand-tune the model, but it is automated, enabling non-experts to build efficient machine learning models. It will democratize AI to a more diverse community.

The project aims to auto-generate both efficient neural networks and their hardware implementations that can generalize to high-dimensional representations, through automatic machine learning (AutoML) techniques. It will design a hardware accelerator to provide more computation per unit cost. The algorithm hardware co-design approach unveils a larger design space that conventional wisdom has been limited to. It is expected to shorten the design cycle of neural architecture search by two orders of magnitude over existing work. With AutoML, powerful hardware, and the co-designed efficient algorithms, it is possible to solve more challenging AI tasks on high-dimensional data that are previously difficult or impossible, hindered by the computation resource, such as videos and 3D point clouds. These techniques give rise to a deeper understanding of the high dimensional representations and produce state-of-the-art neural network architectures that can efficiently run on mobile devices as well as protect user privacy.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Computer and Communication Foundations (CCF)
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Yuanyuan Yang
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Massachusetts Institute of Technology
United States
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