Projects

(IoTJ)A Unified TinyML System for Multi-modal Edge Intelligence and Real-time Visual Perception.

Our research focuses on the software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization and hardware-level arithmetic acceleration.

(ATC2021) Adaptive Quantization-aware Training and Model Compression.

Our research focuses on the software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization and hardware-level arithmetic acceleration.

A Unified Contrastive Representation Learner for Cross-modal Federated Learning Systems.

Our research focuses on the software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization and hardware-level arithmetic acceleration.

(NeurIPS2022) Progressive Network Sparsification and Latent Feature Compression for Scalable Collaborative Learning.

Our research focuses on the software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization and hardware-level arithmetic acceleration.

(AAAI2023)Masked Autoencoders for Occlusion-aware Visual Learners

Our research focuses on the software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization and hardware-level arithmetic acceleration.

Flexible Patch Skip for Real-time Visual Perception.

Our research focuses on the software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization and hardware-level arithmetic acceleration.

Efficient Federated Learning Framework on Heterogeneous Environment

Federated learning (FL) has been proposed as a promising solution for future AI applications with strong privacy protection. It enables distributed computing nodes to collaboratively train models without exposing their own data.

Next generation blockchain system

Our team aims at the next-generation blockchain system with scalability, security, privacy, and intelligence and our proposed architecture is composed of 6 layers as above. In the following, the details of these 6 layers will be explained from top to bottom.

Radiation-free Spine Reconstruction and Posture Analysis Techniques with 3D Imaging

Scoliosis is a sideways curvature of the spine that occurs most often during thegrowth spurt just before puberty. According to the survey and statistics of China Child Development Center, more than 20% teens have scoliosis.

(TC)Heterogeneous Data \& Resource Constraints- Batch Size Adaptation

To tackle non-IID data challenge in FL, we consider to design a new method to improve training efficiency of each client from the perspective of whole training process.