A Scalable and Programmable Architecture for the Continuous Restricted Boltzmann Machine in VLSI

The Continuous Restricted Boltzmann Machine (CRBM) has been attractive as a probabilistic model both useful in biomedical applications and hardware-amenable. To implement a large-scale CRBM system for real applications, the performance of the CRBM under hardware constraints must be investigated. This paper examines the effects of parameter precision on the performance of the CRBM, identifies the required precision for modelling real biomedical data, and finally proposes a scalable and programmable architecture based on which a CRBM-embedded intelligent system can be formed.