There has been a tremendous demand for bringing Deep Neural Network (DNN) powered functionality into Internet of Thing (IoT) devices to enable ubiquitous intelligent "IoT cameras". However, state-of-the-art DNNs have a prohibitive energy cost, making them impractical to be deployed in resource-constrained IoT platforms. This project will develop a novel energy-efficient DNN framework, via a systematic integration of platform, hardware, and algorithm co-design innovations. Despite a growing interest in energy-efficient DNNs, existing techniques lack a systematic optimization across the full stack of design abstraction, from systems through algorithms to hardware implementation. The proposed research advocates an innovative, holistic effort towards energy-efficient and adaptive DNN-powered "IoT cameras" by jointly optimizing the platform-, hardware-, and algorithm-level co-design efforts. On the system level, we will address how to automatically generate and adapt DNN models and implementation, to meet a variety of "IoT devices" application-specific performance needs and device-specific resource constraints. On the hardware level, we will leverage the observed high sparsity in DNN activations for energy-efficient hardware implementations of both DNN training and inference by using low-cost zero predictors and hence bypass unnecessary computations. On the algorithm level, we will develop innovative factorized sparsity regularization in DNN training as well as efficient, controllable adaptive inference mechanisms, fully complementing and closely integrating with our hardware innovations.
The proposed research will advance the scientific domain of each level, from system and algorithm, to hardware and a holistic, systematic cross-level methodology for designing energy-efficient intelligent systems. Progress on this project will enable ubiquitous DNN-powered intelligent functions in a significantly increased number of resource-constrained daily-life devices, across numerous camera-based Internet-of-Things (IoT) applications such as traffic monitoring, self-driving and smart cars, personal digital assistants, surveillance and security, and augmented reality. As camera-based IoT devices penetrate all walks of life, by enabling DNN-powered intelligence to be pervasive in these devices, the proposed research can have a tremendous impact on global societies and economies. The research will be integrated with education on energy efficient deep learning. Educational activities include curriculum development, undergraduate research, and outreach to K-12 students.
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.
