M4: Constrained Systems

M4: Constrained Systems #

This module considers how to make models practical when memory, compute, latency, and energy are limited. It connects model compression with edge deployment and the trade-offs required to retain useful prediction quality.

Module coverage:

• Model Optimisation Techniques: model compression, structured and unstructured pruning, post-training quantisation, and quantisation-aware training.
• Edge Deployment: TinyML, TensorFlow Lite, ONNX Runtime, and knowledge distillation.
• Performance Trade-offs: accuracy versus latency, model size versus throughput, and energy consumption optimisation.

Learning Objectives #

• Compare methods for reducing model size, computation, and memory use.
• Distinguish different quantisation and pruning approaches.
• Explain the role of knowledge distillation and deployment frameworks.
• Evaluate accuracy, latency, throughput, model size, and energy together.

Planned Topics #

This index records the module scope. Chapter pages can be added here as their source material becomes available.