Customising parallelism and caching for machine learning

Inductive logic programming is an attractive and expressive paradigm for machine learning. A drawback of inductive logic programs is their demanding computational requirements. We present an FPGA-based multi-processor architecture aimed at fast execution of such programs. The architecture exploits both coarse-grained parallelism at the query level, and fine-grained parallelism in the unification algorithm. Instructions are not required, and the components are customised for a hypothesis space referring only to ground unit clauses in the background knowledge. It also benefits from a distributed memory hierarchy, with a method for including background knowledge to eliminate instructions. The effectiveness of this architecture is demonstrated using a large organic chemistry data set. The proposed architecture is faster and smaller than our previous design based on multiple instruction processors. A single customised processor at 38MHz can run 9 times faster than a Pentium 4 processor at 1.8GHz; a Xilinx XCV2000E device can accommodate 24 processors running in parallel.