TinyTorch/capstone-ideas

🎓 TinyTorch Capstone Project Ideas

Background: The Capstone Design Problem

Original Issue: Module 20 was "TinyGPT Capstone" but students can already build TinyGPT after Module 13 (Transformers). This made:

• Modules 14-19 (optimization) feel like "optional extras"
• Module 20 anticlimactic ("TinyGPT again?")
• No integration of crucial systems engineering skills

Solution Requirements:

• Must integrate ALL modules 1-19 (especially optimization modules 14-19)
• Must be genuinely exciting and different
• Must demonstrate complete ML systems engineering mastery
• Must create portfolio-worthy deliverables

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🏆 RECOMMENDED: AI Olympics Competition

📁 See: ai-olympics.md

Core Concept: Competitive leaderboard where students optimize TinyTorch models across systems engineering dimensions.

Why This is Best:

• ✅ Natural motivation: Students want to rank high on leaderboards
• ✅ Systems focus: Compete on speed, memory, efficiency - not just accuracy
• ✅ Community building: Creates ongoing engagement and peer interaction
• ✅ Portfolio impact: "I ranked #3 in TinyTorch AI Olympics" is compelling
• ✅ Forces optimization: ALL modules 14-19 become essential for competitive performance

Competition Categories:

• 🏃‍♂️ Speed Demon: Fastest inference
• 💾 Memory Miser: Smallest memory footprint
• 📱 Edge Expert: Best Raspberry Pi performance
• 🔋 Energy Efficient: Lowest power consumption
• 🏆 TinyMLPerf: Overall benchmark champion

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🛠️ Alternative Ideas Considered

1. Edge AI Deployment System

Concept: Deploy optimized neural networks to actual edge hardware (Raspberry Pi)

Pros:

• Integrates all optimization modules (essential for edge constraints)
• Creates tangible deliverable ("I run neural networks on a $35 computer")
• Teaches real-world deployment challenges

Cons:

• Individual project (no community/competition aspect)
• Hardware dependencies (students need Pi)
• Less motivating than competition

2. Multi-Modal AI Assistant

Concept: Combine vision (CNNs) + language (transformers) + optimization for real-time performance

Pros:

• Showcases multiple architectures working together
• Demonstrates practical AI applications
• Requires optimization for real-time performance

Cons:

• Complex scope potentially overwhelming
• Optimization feels secondary to "getting it working"
• Limited portfolio differentiation

3. ML Performance Laboratory

Concept: Comprehensive benchmarking suite comparing different ML frameworks

Pros:

• Heavy focus on profiling and benchmarking skills
• Creates useful tool for community
• Deep systems engineering focus

Cons:

• More about measurement than optimization
• Limited creative expression for students
• May feel academic rather than practical

4. Neural Architecture Search

Concept: Automated model design and optimization system

Pros:

• Cutting-edge research area
• Requires sophisticated optimization
• Highly technical achievement

Cons:

• Very advanced, may be beyond course scope
• Optimization becomes means rather than end
• Difficult to assess fairly

5. Distributed Training System

Concept: Multi-GPU/multi-node training infrastructure

Pros:

• Advanced systems engineering skills
• High industry relevance
• Impressive technical achievement

Cons:

• Requires expensive hardware
• Complex debugging and setup
• May overshadow core ML concepts

6. ML Model Marketplace

Concept: Complete system for sharing/deploying/optimizing models (like Hugging Face)

Pros:

• Full-stack systems engineering
• Practical deployment focus
• Creates useful community resource

Cons:

• Web development skills needed
• Broad scope potentially unfocused
• Less emphasis on optimization techniques

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📊 Evaluation Criteria

Criteria	AI Olympics	Edge Deployment	Multi-Modal	ML Lab	NAS	Distributed	Marketplace
Integrates All Modules	✅✅✅	✅✅	✅	✅	✅	✅	✅
Student Motivation	✅✅✅	✅	✅	⚠️	⚠️	⚠️	✅
Portfolio Impact	✅✅✅	✅✅	✅	✅	✅✅	✅✅	✅
Systems Engineering Focus	✅✅✅	✅✅	✅	✅✅✅	✅	✅✅✅	✅
Implementation Feasibility	✅✅	✅✅✅	✅	✅✅	⚠️	⚠️	✅
Community Building	✅✅✅	⚠️	⚠️	✅	⚠️	⚠️	✅✅
Scalability	✅✅✅	✅	✅	✅	⚠️	⚠️	✅

Legend: ✅✅✅ Excellent, ✅✅ Good, ✅ Adequate, ⚠️ Challenging

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🎯 Final Recommendation

AI Olympics emerges as the clear winner because it:

1. Maximizes student motivation through competitive leaderboards
2. Forces integration of ALL optimization modules (14-19)
3. Creates lasting community beyond individual course completion
4. Produces compelling portfolio artifacts (leaderboard rankings)
5. Scales naturally as more students participate
6. Emphasizes systems engineering over algorithmic implementation

Implementation Priority

1. Phase 1: Design and build leaderboard infrastructure
2. Phase 2: Create standard benchmark evaluation suite
3. Phase 3: Deploy beta version with small student cohort
4. Phase 4: Full launch with all TinyTorch students

Success Metrics

• Participation Rate: % of students who submit to multiple categories
• Optimization Depth: Average number of techniques applied per submission
• Community Engagement: Forum activity, peer collaboration, ongoing submissions
• Portfolio Impact: Industry feedback on graduate capabilities

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📝 Notes for Implementation

Technical Requirements

• Automated submission and evaluation pipeline
• Standard benchmark datasets and environments
• Real-time leaderboard with rich visualizations
• Robust measurement and scoring systems

Educational Integration

• Clear rubrics linking competition performance to course grades
• Structured optimization process through modules 14-19
• Portfolio development guidance and templates
• Peer review and collaboration opportunities

Community Features

• Student profiles and achievement tracking
• Optimization technique sharing and discussion
• Mentorship connections between high performers and struggling students
• Industry guest judging and feedback

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🚀 The AI Olympics transforms TinyTorch from "just another ML course" into a competitive systems engineering community that motivates deep learning, creates lasting engagement, and produces industry-ready graduates.