CRII:CSR: Enabling High-Performance Deep Learning Computing System via Software and Hardware Co-Optimized Reconfiguration

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Deep learning (DL) technology has much success in today's artificial intelligence (AI) applications. However, executions of DL algorithms consume much computational and energy resources because of the large-scale data and complex computation models. The project addresses this challenge with a hardware and software co-optimization strategy. A high-performance deep learning system is enabled with the capability to select automatically the best parameter configurations of advanced algorithms based on the hardware architecture of the computing system. The project seeks to solve a fundamental challenge of a DL based computing system where intensive computations are introduced while the computing resources and the real-time budget are limited. Task 1 proposes an algorithm-driven DL computing system configuration based on a full parameter deep learning compression and a hardware-friendly algorithm deployment. Task 2 investigates an architecture-driven DL computing system configuration based on DL computation performance profiling and modeling on various hardware architectures. The success of the project paves the design foundation of a DL based intelligence system by considering the constraints of data, algorithm, and hardware platform.The project provides benefit to computational intelligence (CI), embedded systems, mobile intelligence, machine learning, and computing architecture. The project will further promote software and hardware co-development towards highly efficient intelligence systems for real-world artificial intelligence applications. The project will also benefit a wide range of communities by means of seminar broadcasts, a short course program, and children and high-school programs. The education plan will enhance existing curricula and pedagogy by integrating interdisciplinary modules with innovative teaching practices. The outcomes of the project, including data and experiments results, will be distributed in the form of journal articles, conference proceedings, workshops, invited presentations and student thesis. The results related with developed curriculum will also be published in appropriate education conferences. The simulators built for validation and simulation code will also be available to the public. Copies of these papers, presentations, models, simulation codes and course notes will be placed on a research page (http://if-lab.org/awards/crii2018) associated with this project.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.