Standard cell layout generation with applied artificial intelligence
A genetic algorithm is utilized to generate routing candidates to which a reinforcement learning model is applied to correct the design rule constraint violations incrementally. A design rule checker provides feedback on the violations to the reinforcement learning model and the model learns how to fix the violations. A layout device placer based upon a simulated annealing method may also be utilized.
1. A routing method for a circuit layout, the method comprising:
executing a genetic routing algorithm to generate routes in the circuit layout;
applying a reinforcement learning model to correct design rule constraint errors arising from the routes; and
applying a number of uncorrected ones of the design rule constraint errors to evolve the chromosomes of a genetic routing algorithm.
2. The method of claim 1 , further comprising:
executing a simulated annealing algorithm to perform device placement in the circuit layout.
3. The method of claim 1 , further comprising applying a number of unrouted terminal pairs to evolve the genetic routing algorithm.
4. The method of claim 1 , wherein the reinforcement learning model comprises a convolutional neural network generating embeddings for at least one policy neural network and a state value neural network.
5. The method of claim 4 , wherein the at least one policy neural network comprises a plurality of fully connected layers and an operation mask.
6. The method of claim 4 , wherein the state value neural network comprises a plurality of fully connected layers.
7. The method of claim 1 , further comprising:
the reinforcement learning model receiving an image of a stick depiction of the circuit layout; and
the reinforcement learning model transforming the image into action probabilities and a state value.
8. The method of claim 7 , wherein the transformation into the action probabilities and the state values is invariant in relation to a width of the stick depiction.
9. The method of claim 1 , wherein a fitness function for the genetic routing algorithm comprises a weighted sum of a number of unrouted terminal pairs in the routes and a number of the design rule constraint errors in the routes.
10. A system comprising:
one or more processors; and
logic that when applied to the one or more processors:
operates a simulated annealing algorithm to perform device placement on a circuit layout;
operates a genetic routing algorithm on the circuit layout to generate a plurality of candidate routed circuit layouts;
operates a reinforced learning model to correct design rule constraint errors in the candidate routed circuit layouts; and
applies feedback of a number of uncorrected ones of the design rule constraint errors in a fitness function to evolve chromosomes of the genetic routing algorithm.
11. The system of claim 10 , wherein the design rule constraint errors are applied to a fitness function of the genetic routing algorithm.
12. The system of claim 11 , wherein a number of unrouted terminal pairs is also applied to the fitness function of the genetic routing algorithm.
13. The system of claim 10 , wherein the reinforcement learning model comprises a convolutional neural network generating embeddings for a plurality of policy neural networks and a state value neural network.
14. The system of claim 13 , wherein the policy neural network comprises a plurality of fully connected layers and an operation mask.
15. The system of claim 13 , further comprising:
a pooling layer; and
the state value neural network comprising a plurality of fully connected layers coupled to receive an output of the pooling layer.
16. The system of claim 10 , the reinforcement learning model configured to:
receive stick depiction images the candidate routed circuit layouts; and
transform the stick depiction images into action probabilities for correcting the design rule constraint errors.
17. The system of claim 16 , wherein the transformation into the action probabilities is invariant in relation to a width of the stick depiction images.
18. The system of claim 10 , the genetic routing algorithm further comprising:
a fitness function comprising a reciprocal of a weighted sum of a number of unrouted terminal pairs in the candidate routed circuit layouts and a number of the design rule constraint errors in the candidate routed circuit layouts.