Machine learning techniques for circuit design debugging
A method includes obtaining input signal values and output signal values from an integrated circuit and providing the obtained input signal values and output signal values to a machine learning model. The method further includes obtaining, from the machine learning model, a predicted set of signal values, wherein the predicted set of signal values correspond to signals internal to the integrated circuit and comparing the predicted set of signal values to one or more expected signal values for debugging the integrated circuit.
1. A method, comprising:
obtaining input signal values and output signal values from an integrated circuit;
providing the obtained input signal values and output signal values to a machine learning model;
obtaining, from the machine learning model, a predicted set of signal values, wherein the predicted set of signal values correspond to signals internal to the integrated circuit;
comparing the predicted set of signal values to one or more expected signal values for debugging the integrated circuit.
2. The method of claim 1 , wherein the machine learning model predicts a finite state machine (FSM) state based on the provided input signal values and output signal values.
3. The method of claim 2 , further comprising obtaining, from the machine learning model, a predicted finite state machine state for the integrated circuit.
4. The method of claim 1 , wherein the machine learning model includes one or more sub-models.
5. The method of claim 4 , wherein the one or more sub-models of the machine learning model predicts an output of a circuit block of the integrated circuit.
6. The method of claim 4 , wherein the one or more sub-models of the machine learning model predicts a finite state machine (FSM) state.
7. The method of claim 1 , wherein the one or more expected signal values are obtained based on simulations of the integrated circuit.
8. A non-transitory program storage device comprising instructions stored thereon to cause one or more processors to:
obtain input signal values and output signal values from an integrated circuit;
provide the obtained input signal values and output signal values to a machine learning model;
obtain, from the machine learning model, a predicted set of signal values, wherein the predicted set of signal values correspond to signals internal to the integrated circuit;
compare the predicted set of signal values to one or more expected signal values for debugging the integrated circuit.
9. The non-transitory program storage device of claim 8 , wherein the machine learning model predicts a finite state machine (FSM) state based on the provided input signal values and output signal values.
10. The non-transitory program storage device of claim 9 , wherein the instructions further cause the one or more processors to obtain, from the machine learning model, a predicted finite state machine state for the integrated circuit.
11. The non-transitory program storage device of claim 8 , wherein the machine learning model includes one or more sub-models.
12. The non-transitory program storage device of claim 11 , wherein the one or more sub-models of the machine learning model predicts an output of a circuit block of the integrated circuit.
13. The non-transitory program storage device of claim 11 , wherein the one or more sub-models of the machine learning model predicts a finite state machine (FSM) state.
14. The non-transitory program storage device of claim 8 , wherein the one or more expected signal values are obtained based on simulations of the integrated circuit.
15. A system for debugging an integrated circuit, comprising:
a memory; and
one or more processors operatively coupled to the memory, wherein the one or more processors are configured to execute instructions causing the one or more processors to:
obtain input signal values and output signal values from the integrated circuit;
provide the obtained input signal values and output signal values to a machine learning model;
obtain, from the machine learning model, a predicted set of signal values, wherein the predicted set of signal values correspond to signals internal to the integrated circuit;
compare the predicted set of signal values to one or more expected signal values for debugging the integrated circuit.
16. The system of claim 15 , wherein the machine learning model predicts a finite state machine (FSM) state based on the provided input signal values and output signal values.
17. The system of claim 16 , wherein the instructions further cause the one or more processors to obtain, from the machine learning model, a predicted finite state machine state for the integrated circuit.
18. The system of claim 15 , wherein the machine learning model includes one or more sub-models.
19. The system of claim 18 , wherein the one or more sub-models of the machine learning model predicts an output of a circuit block of the integrated circuit.
20. The system of claim 18 , wherein the one or more sub-models of the machine learning model predicts a finite state machine (FSM) state.