All Tags
AWS
algorithm-design
architecture
cloud-principles
cost-reduction
data-centric
data-compression
data-processing
deployment
design
edge-computing
energy-footprint
hardware
libraries
locality
machine-learning
management
measured
migration
model-optimization
model-training
performance
queries
rebuilding
scaling
services
strategies
template
workloads
Tactic: Consider Transfer Learning
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: green-ml-enabled-systems
Title
Consider Transfer Learning
Description
Transfer learning means using knowledge gained from one task (a pre-trained model) and applying it to another similar task. This is feasible only if there is an existing pre-trained model available for use. The absence of or reduction in the model training effort in case of fine-tuning results in savings in energy consumption.
Participant
Data Scientist
Related software artifact
Machine Learning Algorithm
Context
Machine Learning
Software feature
Neural Networks
Tactic intent
Improve energy efficiency by using transfer learning with pre-trained models whenever feasible
Target quality attribute
Energy Efficiency
Other related quality attributes
< unknown >
Measured impact
< unknown >
Source
Nitthilan Kanappan Jayakodi, Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa. 2020. Design and Optimization Of Energy-Accuracy Tradeoff Networks For Mobile Platforms Via Pretrained Deep Models. ACM Transactions on Embedded Computing Systems (TECS) 19, 1 (2020), 1–24. [DOI](https://doi.org/10.1145/3366636); Shriram Shanbhag, Sridhar Chimalakonda, Vibhu Saujanya Sharma, and Vikrant Kaulgud. 2022. Towards a Catalog of Energy Patterns in Deep Learning Development. In Proceedings of the International Conference on Evaluation and Assessment in Software Engineering 2022. 150–159. [DOI](https://doi.org/10.1145/3530019.3530035)Graphical representation
