Smart Dim Model

The Smart Dim Model is an experimental model used to predict whether an upcoming screen-dim should go ahead or be deferred. The prediction is based on whether the user is likely to remain inactive or reactivate following a screen-dim. If the user is likely to reactivate, the model would predict the dim should be deferred, otherwise, the model would predict the dim should go ahead.

Model prediction

The input to the model is a list of features that would help predict user activity after the screen is dimmed. Example features are user related features (e.g. activity count) and environment features (e.g. time of day). The model takes in these features and calculates an inactivity-score: the higher the score, the more likely the user will remain inactive. If this inactivity-score is higher than or equal to a dim-threshold (set by an experiment), the model will predict the dim should go ahead; otherwise it will predict the dim should be deferred.

The features used by the model are those metrics logged to UKM by UserActivityUkmLoggerImpl. These metrics and features do not contain any user personal data. They are aggregated when training the model.

Using these metrics, we trained a DNN model. The inferencing code in tf_native_inference.cc consists of the model weights generated by TensorFlow and basic operations to execute the model over an example.

Example preprocessing

The tf_native_inference.cc generated from a TensorFlow model expects input features to be represented as a vector of floats. This conversion is handled by AssistRanker based on the configuration provided in example_preprocessor_config.pb. The feature-to-float conversion depends on the type of the feature. For example, a numerical feature will be converted to a corresponding float (possibly normalized). Categorical features (e.g. enums or bucketized numerical features) will be converted via One-hot encoding. Missing features can also be handled, with configurations specified in the example_preprocessor_config.pb. The configuration can be examined with the print_example_preprocessor_config.py utility:

shell

./components/assist_ranker/print_example_preprocessor_config.py \
  out/Release \
  chrome/browser/ash/power/ml/smart_dim/example_preprocessor_config.pb

Smart dim model interface

Example processing and inactivity-score calculation are all internal details of the model. The public interface of the model is SmartDimModel::ShouldDim(). SmartDimModelImpl::ShouldDim() provides the actual implementation. It takes prediction features (UserActivityEvent::Features) as input and returns UserActivityEvent::ModelPrediction. The returned prediction contains both model response (dim, no-dim or model-error) and quantized values of inactivity-score and dim-threshold. These two values are quantized in the returned result so that they can be logged when necessary.