Designing a binary neural network co-processor

Abstract

A correlation matrix memory (CMM) is a form of binary neural network, that can be used for high-speed approximate search and match operations on large unstructured datasets. Typically, the processing requirements for a CMM do not map efficiently onto a modern processor based system. Therefore, an application specific co-processor is normally used to improve performance. This paper outlines two possible FPGA based co-processors for executing core CMM operations based upon a compact bit vector (CBV) data format. This representation significantly increases a system's storage capacity, but reduces processing performance.