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Tactic: Consider Knowledge Distillation

Tactic sort: Awesome Tactic

Type: Architectural Tactic

Category: green-ml-enabled-systems

Tags: machine-learning model-optimization

Title

Consider Knowledge Distillation

Description

Knowledge distillation is a technique where a large, complex model (teacher) is used to train a smaller, simpler model (student). The goal is to transfer the learned information from the teacher model to the student model, allowing the student model to achieve comparable performance while requiring fewer computational resources. Knowledge distillation improves performance when evaluating accuracy and energy consumption.

Participant

Data Scientist

Related software artifact

Machine Learning Algorithm

Context

Machine Learning

Software feature

Knowledge Distillation

Tactic intent

Improve energy efficiency by apply knowledge distillation of pre-trained models if they are too big for a given task

Target quality attribute

Performance

Other related quality attributes

Accuracy, Energy Efficiency

Measured impact

< unknown >

Source

Shanbhag, S., Chimalakonda, S., Sharma, V. S., & Kaulgud, V. (2022, June). Towards a Catalog of Energy Patterns in Deep Learning Development. In Proceedings of the International Conference on Evaluation and Assessment in Software Engineering 2022 (pp. 150-159). [DOI](https://doi.org/10.1145/3530019.3530035); Haichuan Yang, Yuhao Zhu, and Ji Liu. 2019. Energy-Constrained Compression for Deep Neural Networks Via Weighted Sparse Projection and Layer Input Masking. International Conference on Learning Representations (ICLR) (2019) (ICDCSW). 55–62. [DOI](https://doi.org/10.48550/arXiv.1806.04321)