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+## General API (Level 1)
+
+- Knob:
+  - A name -- constant
+  - kwargs (for knob parameter, for example: 25% in 25% perforation) -- constant
+  - Whether coexist with another knob or not -- method
+    - Useful when we want to support multiple knobs in an op
+
+- Application:
+  - List of operators (just a name) -- constant
+  - List of knobs (each with a name) -- constant
+  - List of knobs (by knob name) for each layer -- constant
+    - (We provide a knob value for "baseline" and sneak that in each layer)
+  - Argparser extra arguments -- static method
+  - How to measure QoS & performance given a configuration -- method
+    - Input? Don't care
+    - Empirical or modeled? Don't care
+    - This is a minimal interface to interact with `opentuner`, nothing ApproxTuner yet.
+
+## Predictive Tuning (Level 2)
+
+Performance model + QoS model (P1, P2)
+
+- Performance model (linear combination of operator cost * speedup of knob)
+  - Requires list of operator cost
+  - Requires table of knob speedup on layer
+
+- QoS P1 (linear in tensor output)
+  - Requires tensor output of each single knob
+
+- QoS P2 (linear in QoS)
+  - Requires QoS of each single knob
+
+- Predict-tunable application
+  - User should inherit from a number of interfaces. Inherit P1Interface -> can use P1, etc.
+  - Detect self capability using `isinstance(self, ...)` and offers argparser extra arguments
+    (or config file entries).
+
+## Predictive Tuning for PyTorch Modules (Level 3)
+
+Automate some definitions using inspection on module
+
+- PyTorch application
+  - *Provides list of operators and cost in FLOPs
+  - *Provides function for running inference on module and combining output
+  - *Implements P2
+  - *Implements P1 if output is tensor
+  - Require a PyTorch module
+  - Require an input dataset
+
+## API usage
+
+- User defines classes for knobs
+  - Perforation, sampling...
+- User defines application
+  - Most general: just `Application`. Useful for tuning a binary, for example.