Continuous-valued probabilistic neural computation in VLSI

As interests in implantable devices and bio-electrical systems grow, intelligent embedded systems become important for extracting useful information from continuous-valued, noisy and drifting biomedical signals at sensory interfaces. Probabilistic generative models utilise stochasticity to represent the natural variability of real data, and therefore suggest a potential approach to this application. However, few probabilistic models are amenable to VLSI implementation. This thesis explores the feasibility of realising continuous-valued probabilistic behaviour in VLSI, which may subsequently underpin an intelligent embedded system. In this research, a probabilistic generative model that can model continuous data, with a simple and hardwareamenable training algorithm, has been developed. Based on stochastic computing units with Gaussian noise inputs, this model can adapt its “internal noise” to represent the variability (external noise) of real data. The training algorithm requires only one step of Gibbs sampling and is thus computationally inexpensive in both software and hardware. The capabilities of the model are demonstrated and explored with both artificial and real data. By translating this probabilistic generative model into VLSI implementation, a VLSI system with continuousvalued probabilistic behaviour and on-chip adaptability is further implemented. This not only demonstrates the feasibility of realising continuous-valued probabilistic behaviour in VLSI, but also provides a platform for studying the utility and on-chip adaptability of continuousvalued probabilistic behaviour in VLSI. The system’s ability both to model and to regenerate continuous data distributions are explored. As the probabilistic behaviour is introduced by artificially-generated noise, this VLSI system demonstrates computation with noise-induced, continuous-valued probabilistic behaviour in VLSI, and points towards a potential candidate for an intelligent embedded system. Declaration of originality I hereby declare that the research recorded in this thesis and the thesis itself was composed and originated entirely by myself in the School of Engineering and Electronics at the University of Edinburgh.