Predicting Memory Compiler Performance Outputs Using Feed-forward Neural Networks

Typical semiconductor chips include thousands of mostly small memories. As memories contribute an estimated 25% to 40% to the overall power, performance, and area (PPA) of a product, memories must be designed carefully to meet the system’s requirements. Memory arrays are highly uniform and can be described by approximately 10 parameters depending mostly on the complexity of the periphery. Thus, to improve PPA utilization, memories are typically generated by memory compilers. A key task in the design flow of a chip is to find optimal memory compiler parametrizations that, on the one hand, fulfill system requirements while, on the other hand, they optimize PPA. Although most compiler vendors also provide optimizers for this task, these are often slow or inaccurate. To enable efficient optimization in spite of long compiler runtimes, we propose training fully connected feed-forward neural networks to predict PPA outputs given a memory compiler parametrization. Using an exhaustive search-based optimizer framework that obtains neural network predictions, PPA-optimal parametrizations are found within seconds after chip designers have specified their requirements. Average model prediction errors of less than 3%, a decision reliability of over 99%, and productive usage of the optimizer for successful, large volume chip design projects illustrate the effectiveness of the approach.