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Module 19 Updates: - Added Section 4.4: MLPerf Principles & Methodology - Explains MLPerf framework (industry-standard benchmarking) - Teaches Closed vs Open Division concepts - Covers reproducibility and standardization requirements - References TinyMLPerf for embedded systems - Prepares students for professional ML benchmarking Module 20 Updates: - Rebranded as TinyMLPerf Competition (from generic competition) - Emphasizes MLPerf Closed Division rules throughout - Section 1: TinyMLPerf rules and what is/isnt allowed - Section 2: Official baseline following MLPerf standards - Section 3: Complete workflow following MLPerf methodology - Section 4: Submission template with MLPerf compliance Pedagogical Improvement: - Grounds capstone in real-world MLPerf methodology - Students learn industry-standard benchmarking practices - Competition has professional credibility - Clear rules ensure fair comparison - Reproducibility and documentation emphasized