Abstract
The recognition of bio-medical signals needs to tolerate with the impact of environmental noise. the diffusion network (DN), proposed by Movellan in 2002, involves a stochastic process, which is capable of reflecting the variance of bio-medical signals in real-time. The reconstruction of continuous time, continuous valued signal is hence feasible. This research aims at the VLSI implementation of DN. As the technology evolves, the operating voltages of integrated circuits are further reduced, so are the dynamic ranges of DN variables. The limited operating range makes the learning of signals difficult. The log-domain concept circumvents the problem as well as reduces the power consumption of circuits. The dynamic range of the state variables in DN can operate over several decades without saturation. The log-domain circuit exploits the exponential I-V relationships of MOS operated in sub-threshold region. The original state variables of the neurons are defined as the drain currents. The compressed states are derived from the gate voltage of the transistor. Though the log-domain translation, the diffusion network can be presented in an alternative form which is manipulating with the compressed states. Moreover, the state variables can operate over several decades without saturating the circuits. Before implementing the diffusion network with integrated circuits, we perform the numerical simulations to ensure the dynamic ranges of parameters of different signals. These values are then converted to the circuit domain so that the log-domain diffusion network can be implemented and plenty of signals can be learned with circuits. The dissertation implements the diffusion network from the hardware’s perspective. The log-domain concepts are employed in most circuit designs to enlarge the available dynamic ranges. As a result, versatile signals can be recognized with the proposed hardware.