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fix(efficient_ai): update section label to match renamed heading

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- Changed section ID from 'sec-efficient-ai-efficiency-tradeoffs-e140'
  to 'sec-efficient-ai-resource-constrained-tradeoffs-e140'
- Ensures section label consistency with 'Resource-Constrained Trade-offs' heading
- Completes the fix for issue #960
This commit is contained in:
Vijay Janapa Reddi
2025-09-03 19:44:08 -04:00
parent ed2bf0a5ef
commit 2c349dbe98
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quarto/contents/core/efficient_ai/efficient_ai.qmd
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@@ -1111,7 +1111,7 @@ Data efficiency strengthens both compute and algorithmic efficiency by enabling
Together, the interdependence of these efficiencies enables cloud-based systems to achieve a balance of performance, scalability, and cost-effectiveness. By optimizing model, compute, and data dimensions in harmony, cloud deployments become a cornerstone of modern AI applications, supporting millions of users with efficiency and reliability.
#### Resource-Constrained Trade-offs {#sec-efficient-ai-efficiency-tradeoffs-e140}
#### Resource-Constrained Trade-offs {#sec-efficient-ai-resource-constrained-tradeoffs-e140}
In many machine learning applications, efficiency is not merely a goal for optimization but a prerequisite for system feasibility. Extreme resource constraints, such as limited computational power, energy availability, and storage capacity, demand careful trade-offs between algorithmic efficiency, compute efficiency, and data efficiency. These constraints are particularly relevant in scenarios where machine learning models must operate in low-power embedded devices, remote sensors, or battery-operated systems.