Mapping the Diffusion Network into a stochastic system in Very Large Scale Integration

The Diffusion Network (DN) is a probabilistic model capable of recognising continuous-time, continuous-valued biomedical data. As the stochastic process of the DN is described by stochastic differential equations, realising the DN with analogue circuits is important to facilitate real-time simulation of a large network. This paper presents the translation of the DN into analogue Very Large Scale Integration (VLSI). With extensive simulation, the dynamic ranges of parameters and their representation in VLSI are identified. The VLSI circuits realising the stochastic unit of the DN are further designed and interconnected to form a stochastic system using noise to induce stochastic dynamics in VLSI. The circuit simulation demonstrate that the VLSI translation of the DN is satisfactory and the DN system is capable of using noise-induced stochastic dynamics to regenerate various types of continuous-time sequences.