Scalable acceleration of inductive logic programs

Abstract

Inductive logic programming systems are an emerging and powerful paradigm for machine learning which can make use of background knowledge to produce theories expressed in logic. They have been applied successfully to a wide range of problem domains, from protein structure prediction to satellite fault diagnosis. However, their execution can be computationally demanding. We introduce a scalable FPGA-based architecture for executing inductive logic programs, such that the execution speed largely increases linearly with respect to the number of processors. The architecture contains multiple processors derived from Warren's Abstract Machine, which has been optimised for hardware implementation using techniques such as instruction grouping and speculative assignment. The effectiveness of the architecture is demonstrated using the mutagenesis data set containing 12000 facts of chemical compounds.