From 68d446edcf87cc66c6d836c18a3dc3953ed5432e Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Sun, 22 Feb 2026 13:19:43 -0500 Subject: [PATCH] docs(module 19): add MLPerf trademark attribution and pedagogical framing MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - MLPerf® is a trademark of MLCommons - This module teaches the principles of MLPerf-style benchmarking --- tinytorch/src/19_benchmarking/19_benchmarking.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tinytorch/src/19_benchmarking/19_benchmarking.py b/tinytorch/src/19_benchmarking/19_benchmarking.py index 5d88b92fb..0b3ad929b 100644 --- a/tinytorch/src/19_benchmarking/19_benchmarking.py +++ b/tinytorch/src/19_benchmarking/19_benchmarking.py @@ -2108,7 +2108,10 @@ if __name__ == "__main__": """ ### MLPerf - Standardized Industry Benchmarking -MLPerf provides standardized benchmarks that enable fair comparison across different systems, similar to how MLPerf works for larger models. This is crucial for reproducible research and industry adoption. +MLPerf® is a trademark of MLCommons. This module provides MLPerf-style standardized +benchmarks that enable fair comparison across different systems, similar to how the +official MLPerf suite works for larger models. This is important for reproducible +research and industry adoption. ### Why Standardization Matters @@ -2206,6 +2209,9 @@ class MLPerf: """ MLPerf-style standardized benchmarking for edge ML systems. + MLPerf® is a trademark of MLCommons. This module teaches the principles of + MLPerf-style benchmarking through a simplified suite inspired by MLPerf Tiny. + Provides fixed benchmark configurations with target thresholds, standardized measurement protocols, and compliance reporting.