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Hierarchical Automatic Power Plane Generation with Genetic Optimization and Multilayer Perceptron
Our automatic power plane generation approach is based on genetic optimization combined with a multilayer perceptron and is able to automatically generate power planes across a diverse set of problems with varying levels of difficulty.
Improving Adversarial Robustness in Weight-quantized Neural Networks
Neural networks are getting deeper and more computation-intensive nowadays.
Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.
Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing
Physical design and production of Integrated Circuits (IC) is becoming increasingly more challenging as the sophistication in IC technology is steadily increasing.
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision
The key advantage of this approach is that the router can learn a policy in an offline setting with supervision, while improving the run-time performance nearly 100x over the genetic solver.
Attention Routing: track-assignment detailed routing using attention-based reinforcement learning
We also discover a similarity between the attention router and the baseline genetic router in terms of positive correlations in cost and routing patterns, which demonstrate the attention router's ability to be utilized not only as a detailed router but also as a predictor for routability and congestion.
