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: Enhance Model Sparsity
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: green-ml-enabled-systems
Title
Enhance Model Sparsity
Description
Enhancing sparsity of a machine learning model means reducing the number of model parameters or setting their values to zero. For example, weight sparsification involves identifying and removing unnecessary or less important weights in a neural network. Enhancing model sparsity decreases the complexity of the model and consequently reduces requirements for storage and memory. Therefore, it also results in lower power consumption.
Participant
Data Scientist
Related software artifact
Machine Learning Algorithm
Context
Machine Learning
Software feature
< unknown >
Tactic intent
Improve energy efficiency by removing unnecessary or less important weights in neural networks
Target quality attribute
Energy Efficiency
Other related quality attributes
Accuracy
Measured impact
Removing unnecessary or less important weights in neural networks lowers energy consumption.
Source
Xiangyu Yang, Sheng Hua, Yuanming Shi, Hao Wang, Jun Zhang, and Khaled B. Letaief. 2020. Sparse Optimization for Green Edge AI Inference. Journal of Communications and Information Networks 5, 1 (2020), 1–15. (DOI: https://doi.org/10.23919/JCIN.2020.9055106)Graphical representation
