low-power-ml

Vocabulary Word

Definition

'Low-power-ml' refers to the use of machine learning techniques that require less power. This is important for devices running on batteries or places with limited power resources.

Examples in Different Contexts

For embedded systems, 'low-power ML' involves integrating machine learning capabilities that require low energy use, essential for long-lasting battery life in embedded devices. An embedded systems engineer might explain, 'Our low-power ML approach ensures our environmental sensors can run for months without a recharge.'

Practice Scenarios

Tech

Scenario:

Our new wearable device needs a significant battery-life improvement. We should consider strategies that reduce power consumption while maintaining performance.

Response:

I agree. Implementing low power ML can optimize energy consumption and prolong device battery life.

Space-Technology

Scenario:

The energy scarcity experienced by our space probes require highly efficient computational models. It's quite a challenge given the massive data they need to analyze as well.

Response:

Absolutely, we can use low power ML models to ensure efficient power consumption while dealing with large data sets.