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Tactic: Use Quantization-Aware Training
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: green-ml-enabled-systems
Title
Use Quantization-Aware Training
Description
Quantization-aware training is a technique used to train neural networks to convert data types to lower precision. The idea is to use fixed-point or integer representations instead of the more commonly used higher-precision floating-point representations. This improves the performance and energy efficiency of the model in federated learning.
Participant
Data Scientist
Related software artifact
Model
Context
Machine Learning
Software feature
Model Training
Tactic intent
Improve energy efficiency by using quantization-aware training to convert high-precision data types to lower precision
Target quality attribute
Accuracy
Other related quality attributes
Energy Efficiency
Measured impact
< unknown >
Source
Minsu Kim, Walid Saad, Mohammad Mozaffari, and Merouane Debbah. 2021. On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks. In ICC 2022 - IEEE International Conference on Communications. 2194–2199. [DOI](https://doi.org/10.1109/ICC45855.2022.9838362); Martino Sorbaro, Qian Liu, Massimo Bortone, and Sadique Sheik. 2020. Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications. Frontiers in Neuroscience 14 (2020), 662. [DOI](https://doi.org/10.3389/fnins.2020.00662)Graphical representation
![]( /AwesomeAndDarkTactics/assets/diagrams/use-quantization-aware-training.png)