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Improve module naming for clarity

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Changes:
- Module 09: 'Spatial' → 'Spatial (CNNs)' in TOC for clarity
- Module 20: 'TinyMLPerf' → 'MLPerfEdu' to avoid confusion
  * TinyMLPerf is a real benchmark for edge devices
  * MLPerfEdu clearly indicates educational competition
  * More accurate descriptor for this capstone
- Fixed 'Performance Tier' → 'Optimization Tier' in Module 20 objectives

Better naming makes the course structure clearer for students.
This commit is contained in:
Vijay Janapa Reddi
2025-11-07 18:15:39 -05:00
parent 80983d9ad1
commit 229971ef85
2 changed files with 6 additions and 6 deletions
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4
book/_toc.yml
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@@ -47,7 +47,7 @@ parts:
- file: chapters/08-dataloader
title: "08. DataLoader"
- file: chapters/09-spatial
title: "09. Spatial"
title: "09. Spatial (CNNs)"
- file: chapters/10-tokenization
title: "10. Tokenization"
- file: chapters/11-embeddings
@@ -75,7 +75,7 @@ parts:
- caption: 🏅 Capstone Project
chapters:
- file: chapters/20-capstone
title: "20. AI Olympics"
title: "20. MLPerfEdu Competition"
- caption: 🌍 Community
chapters:

8
book/chapters/20-capstone.md
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@@ -1,19 +1,19 @@
---
title: "TinyMLPerf Competition - Your Capstone Challenge"
description: "Apply all optimizations in a standardized MLPerf-inspired competition"
title: "MLPerfEdu Competition - Your Capstone Challenge"
description: "Apply all optimizations in a standardized MLPerf-inspired educational competition"
difficulty: 5
time_estimate: "10-20 hours"
prerequisites: ["All modules 01-19"]
next_steps: []
learning_objectives:
- "Apply all Performance Tier optimizations to a standardized benchmark"
- "Apply all Optimization Tier techniques to a standardized benchmark"
- "Implement either Closed Division (optimize given model) or Open Division (innovate architecture)"
- "Generate validated submission with normalized metrics"
- "Demonstrate complete ML systems engineering skills"
- "Compete fairly across different hardware platforms"
---
# 20. TinyMLPerf Competition
# 20. MLPerfEdu Competition
**🏆 CAPSTONE** | Difficulty: ⭐⭐⭐⭐⭐ (5/5 - Ninja Level) | Time: 10-20 hours