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 project, we have additional documentation in the [docs](docs/) folder.
 
 *   [Benchmarks](benchmarks/README.md)
+*   [Profiling](docs/profiling.md)
 *   [Memory Management](docs/memory_management.md)
 *   [New Platform Support](docs/new_platform_support.md)
 *   [Software Emulation with Renode](docs/renode.md)
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+   * [Profiling](#profiling)
+      * [API](#api)
+      * [Per-Op Profiling](#per-op-profiling)
+      * [Subroutine Profiling](#subroutine-profiling)
+
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+
+# Profiling
+
+This doc outlines how to use the TFLite Micro profiler to gather information
+about per-op invoke duration and to use the profiler to identify bottlenecks
+from within operator kernels and other TFLite Micro routines.
+
+## API
+
+The MicroInterpreter class constructor contains and optional profiler argument.
+This profiler must be an instance of the tflite::Profiler class, and should
+implement the BeginEvent and EndEvent methods. There is a default implementation
+in tensorflow/lite/micro/micro_profiler.cc which can be used for most purposes.
+
+## Per-Op Profiling
+
+There is a feature in the MicroInterpreter to enable per-op profiling. To enable
+this, provide a MicroProfiler to the MicroInterpreter's constructor then build
+with a non-release build to disable the NDEBUG define surrounding the
+ScopedOperatorProfile within the MicroInterpreter.
+
+## Subroutine Profiling
+
+In order to further dig into performance of specific routines, the MicroProfiler
+can be used directly from the TFLiteContext or a new MicroProfiler can be
+created if the TFLiteContext is not available where the profiling needs to
+happen. The MicroProfiler's BeginEvent and EndEvent can be called directly, or
+wrapped using a [ScopedProfile](../../lite/core/api/profiler.h).