Talk Title: Efficient Neural Question Answering for Heterogeneous Platforms
Talk Abstract: Natural language processing (NLP) systems power many real-world applications like Alexa, Siri, or Google and Bing. Deep learning NLP systems are becoming more effective due to increasingly larger models with multiple layers and millions to billions of parameters. It is challenging to deploy these systems because they are compute-intensive, consume much more energy, and cannot run on mobile devices. In this talk, I will present two works on optimizing efficiency in question answering systems and my current research in studying large NLP models’ energy consumption. First, I will introduce DeQA, which provides an on-device question-answering capability to help mobile users find information more efficiently without privacy issues. Deep learning based QA systems are slow and unusable on mobile devices. We design the latency- and memory- optimizations widely applicable for state-of-the-art QA systems to run locally on mobile devices. Second, I will present DeFormer, a simple decomposition-based technique that takes pre-trained Transformer models and modifies them to enable faster inference for QA for both the cloud and mobile. Lastly, I will introduce how we can accurately measure the energy consumption of NLP models using hardware power meters and build reliable energy estimation models by abstracting meaningful features of the NLP workloads and profiling runtime resource usage.
Bio: Qingqing Cao is a graduating Computer Science Ph.D. candidate at Stony Brook University. His research interests include natural language processing (NLP), mobile computing, and machine learning systems. He has focused on building efficient and practical NLP systems for both edge devices and the cloud, such as on-device question answering (MobiSys 2019), faster Transformer models (ACL 2020), and accurate energy estimation of NLP models. He has two fantastic advisors: Prof. Aruna Balasubramanian and Prof. Niranjan Balasubramanian. He is looking for postdoc openings in academia or research positions in the industry.