Implementation of LSTM on the MNN Framework
Project Description
Deep learning has had a tremendous impact in computer vision, natural language processing, and audio signal processing. Due to this success, further efforts have concentrated on porting these deep models on resource-constrained devices such as phones, wearables, and, more recently, micro- controllers. While on-device inference has now been widely explored, highly fast and efficient on-device training remains an open area of investigation. In this project, you will develop a framework to enable efficient training on smartphones, wearable platforms. This will extend an open source mobile framework (MNN) for enabling efficient GPU training on Android.

Contact: If you are interested in this project, please contact Young D. Kwon (ydk21@cam.ac.uk), Jagmohan Chauhan (jc2161@cam.ac.uk/J.Chauhan@soton.ac.uk), and Pan Hui (panhui@cse.ust.hk) for more details.

Reference papers (can be a good starting point)
[1] 2020. MLSys. MNN: A Universal and Efficient Inference Engine. Xiaotang Jiang et al.

Supervisor
HUI Pan
Quota
3
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles
Required skills/aptitudes to successfully complete the project are:

1. Self-motivated and proactive, which means you should be able to set your own milestones and finish them on time.

2. Understanding of frameworks for the development (e.g., Android), basic knowledge of how neural networks work is a plus.
Applicant's Learning Objectives
Benefits of participating in this project are:

1. Opportunity to gain hands-on experience in on-device ML/DL research.

2. Opportunity to tackle a challenging problem.

3. Opportunity for having a top-tier academic publication, depending on the quality and the amount of novel contribution of the project.
Complexity of the project
Challenging