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+# IREE Benchmarks
+
+This directory contains configuration definition for IREE's continuous
+benchmarks suite. Benchmark results are posted to https://perf.iree.dev.
+
+The https://buildkite.com/iree/iree-benchmark Buildkite pipeline runs on each
+commit to the `main` branch and posts those results to the dashboard. The
+pipeline also runs on pull requests with the `buildkite:benchmark` label,
+posting results compared against their base commit as comments.
+
+## Types of benchmarks
+
+```
+├── TensorFlow
+│     * models authored in TensorFlow and imported with `iree-import-tf`
+└── TFLite
+      * models converted to TensorFlow Lite and imported with `iree-import-tflite`
+```
+
+## Adding new benchmarks
+
+### Machine learning model latency
+
+1. Pick the model you want to benchmark and find its source, which could be
+   a Python script, TensorFlow SavedModel from https://tfhub.dev/, TensorFlow
+   Lite FlatBuffer, or some other format with a supported path into IREE. The
+   model can optionally include trained weights if those are important for
+   benchmarking.
+
+2. Import the model into an MLIR file that IREE can compile using the core
+   `iree-translate` tool. For TensorFlow models use `iree-import-tf`, for
+   TensorFlow Lite models use `iree-import-tflite`, etc. Take notes for where
+   the model came from and how it was imported in case the MLIR file needs to
+   be regenerated in the future.
+
+   We may further automate this over time, such as by importing from Python
+   sources as part of the benchmarks pipeline directly (see
+   https://github.com/google/iree/issues/6942). For now, here are some
+   references:
+
+   * https://gist.github.com/antiagainst/35b0989bd0188dd9df4630bb0cf778f2
+   * https://colab.research.google.com/gist/ScottTodd/10838c0ccc87fa6d1b1c72e0fabea064/iree-keyword_spotting_streaming-benchmarks.ipynb
+
+3. Package the imported .mlir model file(s) for storage (see
+   [iree_mlir_benchmark_suite.cmake](../build_tools/cmake/iree_mlir_benchmark_suite.cmake)
+   and [download_file.py](../scripts/download_file.py)), then upload them to the
+   `iree-model-artifacts` Google Cloud Storage bucket with the help of a team
+   member. Files currently hosted in that bucket can be viewed at
+   https://storage.googleapis.com/iree-model-artifacts/index.html.
+
+4. Edit the appropriate `CMakeLists.txt` file under this directory to include
+   your desired benchmark configuration with the `iree_mlir_benchmark_suite`
+   function. You can test your change by running the
+   https://buildkite.com/iree/iree-benchmark pipeline on a GitHub pull request
+   with the `buildkite:benchmark` label.
+
+5. Once your changes are merged to the `main` branch, results will start to
+   appear on the benchmarks dashboard at https://perf.iree.dev.
+
+### Other project metrics
+
+TODO(#6161): Collect metrics for miscellaneous IREE system states