Recent advancements in deep learning hardware and algorithms are providing computers with unprecedented levels of human-like intelligence for applications such as self-driving cars, patient diagnosis and treatment, speech processing, strategy games, and education. Traditional deep learning algorithms rely on powerful computers tethered to the cloud which incurs a large communication overhead, requires extensive computing resources, and compromises privacy and security. There is a strong consensus among experts that the next frontier in deep learning will be highly-efficient neural network processors running on mobile platforms. This project aims at developing a compact and low power alternative to conventional deep learning computing hardware, specifically targeted for edge devices. The proposed approach is based on a novel computing concept called time-based circuits, which can deliver a similar level of inference performance at only a fraction of the power consumption compared to traditional methods. Throughout the project, the investigators will consider transferring the new neural network computing methods to industry. The new time-based deep learning computation methods will be incorporated into the graduate and undergraduate curricula, as well as K-12 outreach activities, of the electrical engineering and computer science departments at the University of Minnesota.
This project will focus on both hardware and software techniques for enabling deep learning applications on resource-constrained mobile platforms. On the hardware side, the team will demonstrate a prototype low-power deep neural network processor where internal operations such as convolution, pooling, and activation functions are performed entirely in the time domain. On the software side, the team will develop pruning, approximation, and hybrid approaches that can effectively reduce the complexity of deep neural networks with minimal impact on the overall inference accuracy. A unique aspect of this project is the continual interaction between the hardware and software groups to deliver the first fully time-based deep neural network engine targeted for edge devices.
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.
