SHF: Small: Dynamic Gating and Adaptation of Deep Neural Networks for Efficient Inference and Training

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The past half-decade has seen unprecedented growth in machine learning with deep neural networks (DNNs), which now represent the state-of-the-art in many AI applications. However, existing DNN models require substantial memory and computing power, which greatly limit their use in resource-constrained systems such as mobile and IoT devices. This project will develop new algorithms and hardware to significantly improve the efficiency of DNNs, and represents an important step towards enabling fast and adaptive DNN executions even in resource-limited environments. In that sense, this project has the potential to enable a wider deployment of machine learning, which will play a critical role in many aspects of the future smart society. The research project will provide research training opportunities to the students as well as new curriculum development by leveraging existing resources at Cornell, e.g., summer camps as well as an outreach programs for high-school students including women.This project aims to significantly improve the efficiency of DNNs, while maintaining high accuracy, by co-developing algorithm optimizations and an efficient hardware-accelerator architecture. While there exist many lines of work on reducing DNN execution costs, the majority of these techniques are designed primarily to improve inference and perform static optimizations that reduce computation uniformly for all inputs or only exploit a limited form of dynamic sparsity, namely zeros. This project aims to enable new performance-accuracy trade-off points for DNNs that are not possible today by exploiting general forms of dynamic sparsity that are specific to each input at run-time. More specifically, the project plans to investigate input-specific gating techniques that can remove redundant computations for both training and inference, develop dynamic quantization techniques that do not require training data, and design an efficient and unified hardware accelerator architecture that provides both real-world performance and energy improvements.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.