SMLP: Symbolic Machine Learning Prover (User Manual)

TL;DR

SMLP is an open source symbolic reasoning tool that explores and optimizes machine learning models under constraints using SMT and probabilistic methods, applicable to hardware design analysis.

Contribution

Introduces SMLP, a versatile tool combining symbolic reasoning with probabilistic exploration for ML model optimization and verification.

Findings

Applied at Intel for hardware design optimization.

Supports various models including NNs, polynomials, and trees.

Uses SMT solvers for reasoning and optimization.

Abstract

SMLP: Symbolic Machine Learning Prover an open source tool for exploration and optimization of systems represented by machine learning models. SMLP uses symbolic reasoning for ML model exploration and optimization under verification and stability constraints, based on SMT, constraint and NN solvers. In addition its exploration methods are guided by probabilistic and statistical methods. SMLP is a general purpose tool that requires only data suitable for ML modelling in the csv format (usually samples of the system's input/output). SMLP has been applied at Intel for analyzing and optimizing hardware designs at the analog level. Currently SMLP supports NNs, polynomial and tree models, and uses SMT solvers for reasoning and optimization at the backend, integration of specialized NN solvers is in progress.