We conduct research on system and architectural issues for accelerating various applications such as deep learning, compression algorithms and graph processing, especially on FPGAs and GPUs. Some of the on-going research topics are listed below. However, you're free to bring your own exciting topic.
With no doubt the most popular accelerator for AI nowadays is GPU. However the world is heading towards the next step: AI-specific accelerators. There is much room to improve in terms of accelerator designs. For example, optimizing dataflow, utilizing sparse network structure, or processing-in-memory techniques.
Designing a neural architecture, especially in relation with specialized accelerators (i.e. NPUs) is a difficult and time-consuming task. Neural architecture search aims to solve this problem in a way that everyone had in mind: designing DNNs using DNNs.
To utilize multiple devices (i.e., GPUs) for high-speed DNN training, it's common to employ distributed learning. There are still many ways to improve current distributed learning methods: Devising a new communication algorithm, smartly pipelining the jobs, or changing the ways that devices synchronize.
Most of the cloud companies now provide deep-learning-as-a-service (DLaaS). One of the significant issues on it is that many customers want to keep their information (network, dataset) secure. Despite the strong security offered by the cloud companies, people will want to have their information private. We study ways to keep the customer network or dataset in private, while still being able to utilize the cloud-provided computing power.