M4: Constrained Systems

Mon, 01 Jan 0001 00:00:00 +0000

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 #

Chapter	Topic	Availability
12	Model Compression Foundations	Planned
13	Quantisation and Edge Intelligence	Planned
14	Pruning and Deep Compression	Planned
15	Optimisation Landscape and Small Language Models	Planned
16	Energy-Constrained ML	Planned

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

Pruning on Arshad Siddiqui

M4: Constrained Systems

M4: Constrained Systems #

Learning Objectives #

Planned Topics #