Machine Learning for Resource Constrained Systems

Learning Outcomes

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Appraise different ways to optimize machine learning for edge computing systems including compression and accelerators
• Building and Training ML models using different machine learning libraries

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Apply knowledge learned from the module to create real world applications and solve issues related to enabling efficient deep learning on edge computing systems
• Effectively communicate technical work
• Communicate a range of issues around edge computing/TinyML to/for a non-technical audience.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Issues and approaches to optimize machine learning models for resource-constrained devices
• Software development tools and frameworks for machine learning on embedded devices;
• How to conceive and design machine learning application on embedded devices
• Fundamentals of machine learning on embedded devices

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Describe trade-offs between the resources and computational complexities of machine learning models.
• Program machine learning applications on embedded systems

This module will look at the theoretical and practical application of machine learning techniques on resource constrained embedded devices. Topics will include:

Fundamentals of machine learning and deep learning on embedded devices.

Challenges of implementing machine learning on resource-constrained devices.

System Performance Trade-Offs.

Model Optimization techniques such as compression and pruning.

Accelerators such as Neural Processing Units and GPU.

Optimization techniques specific to CNN, RNNs and Transformer based models.

Hardware and software tools/frameworks.

Deep learning compilers.

Building, Training and deploying a model

Different Learning Setups (e.g. Transfer, Federated)

Case studies and Applications of Edge computing in domains of computer vison, audio signal processing and NLP.

Lectures and labs/tutorials form the basis for the content delivery for this module. The students will be using open-source deep learning frameworks and will work with real hardware.

Summative

This is how we’ll formally assess what you have learned in this module.