Abstract: Computer-implemented methods using machine learning are provided for generating an estimated cache performance of a cache configuration. A neural network is trained using, as inputs, a set of memory access parameters generated from a non-cycle-accurate simulation of a data processing system comprising the cache configuration and a cache configuration value, and using, as outputs, cache performance values generated by a cycle-accurate simulation of the data processing system comprising the cache configuration. The trained neural network is then provided with sets of memory access parameters generated from a non-cycle-accurate simulation of a proposed data processing system and a selected cache configuration and generates estimated cache performance values for that selected cache configuration.

Abstract: Computer-implemented methods using machine learning are provided for generating an estimated cache performance of a cache configuration. A neural network is trained using, as inputs, a set of memory access parameters generated from a non-cycle-accurate simulation of a data processing system comprising the cache configuration and a cache configuration value, and using, as outputs, cache performance values generated by a cycle-accurate simulation of the data processing system comprising the cache configuration. The trained neural network is then provided with sets of memory access parameters generated from a non-cycle-accurate simulation of a proposed data processing system and a selected cache configuration and generates estimated cache performance values for that selected cache configuration.