From ed2bf0a5ef2a213ddfe0fbe128231566299dde66 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Wed, 3 Sep 2025 19:31:53 -0400 Subject: [PATCH] fix(efficient_ai): resolve duplicate 'Efficiency Trade-offs' heading - Renamed subheading at line 1114 to 'Resource-Constrained Trade-offs' - Eliminates redundancy with main section heading at line 1182 - Improves content flow and reduces repetitive structure - Addresses issue #960 --- quarto/contents/core/efficient_ai/efficient_ai.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quarto/contents/core/efficient_ai/efficient_ai.qmd b/quarto/contents/core/efficient_ai/efficient_ai.qmd index 47d793936..9216d2958 100644 --- a/quarto/contents/core/efficient_ai/efficient_ai.qmd +++ b/quarto/contents/core/efficient_ai/efficient_ai.qmd @@ -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. -#### Efficiency Trade-offs {#sec-efficient-ai-efficiency-tradeoffs-e140} +#### Resource-Constrained Trade-offs {#sec-efficient-ai-efficiency-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.