Welcome to AISys!

AI Accelerators

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.

Distributed Deep Learning

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.

Neural Network Acceleration and Compression

Efficient deployment and training of deep neural networks require reducing both computation and memory overhead. Our research focuses on advanced quantization and compression techniques to accelerate both inference and training across a wide range of models, including large language models (LLMs), diffusion-based image generation, graph neural networks, and spiking neural networks. We aim to enable fast and resource-efficient learning and inference on various hardware platforms without significant loss of accuracy.

System Optimization and Acceleration

We focus on optimizing and accelerating diverse programs and applications across emerging commercial hardwares including GPUs, CXL, and PIM (Processing-In-Memory). Our research spans multiple domains: communication libraries, graph processing algorithms, vector databases, and scientific applications. We optimize system software through a combination of hardware-aware acceleration and algorithmic optimization to achieve significant performance improvements for real-world workloads.