SMLP: Symbolic Machine Learning Prover
This is an incremental method for analyzing and optimizing systems like hardware designs, based on sampling and modeling.
SMLP is a tool that tackles system exploration by combining statistical data analysis with machine learning models in a feedback loop, applied to optimize hardware designs at Intel.
Symbolic Machine Learning Prover (SMLP) is a tool and a library for system exploration based on data samples obtained by simulating or executing the system on a number of input vectors. SMLP aims at exploring the system based on this data by taking a grey-box approach: SMLP combines statistical methods of data exploration with building and exploring machine learning models in close feedback loop with the system's response, and exploring these models by combining probabilistic and formal methods. SMLP has been applied in industrial setting at Intel for analyzing and optimizing hardware designs at the analog level. SMLP is a general purpose tool and can be applied to systems that can be sampled and modeled by machine learning models.