Stochastic Neural Computation



A. Stochastic Neural VLSI for Learning Stochastic Equilibriums

Introduction :Probabilistic neural computation refers to brain-inspired algorithms that are based on computing units (called neurons) whose inputs merely decide their "output probabilities". Such probabilistic behaviour enables probabilistic models to generalise natural variability of real data by the use of its internal stochasticity. Probabilistic models thus have been shown promising for modelling noisy biomedical signals. This project investigates the feasibility of implementing an intelligent system-on-a-chip based on the Continuous Restricted Boltzmann Machine(CRBM), a probabilistic model that has been shown both useful for modeling biomedical data as its stochastic equilibriums and hardware-friendly. A programmable, scalable CRBM system will be implemented for practical biomedical applications. The robustness of a large-scale CRBM system against substrate noise and computational errors will also be investigated.

 

Characteristics : Matlab or C programming will be used to conclude the specifications of modular circuits for a scalable CRBM system, and to simulate the behaviour of a large-scale probabilistic system. A large-scale CRBM system will be finally implemented and tested.

Pre-requisites:Experience in Matlab or C programming, Knowledge in Electronics and mixed-mode VLSI design

Professional skills obtained:Knowledge in probabilistic models, Skills in translating algorithms into VLSI implementation, Methodology in designing VLSI system-on-a-chip. 

Fig. 1 : (a)The modular CRBM (mCRBM) microsystem enabling a large CRBM network to be formed by interconnecting multiple chips (b) The mCRBM microsystem can learn to regenerate the three types of noisy spikes and then (c) project the data into three separate clusters in three-dimensional space (d) The Diffusion Network (DN) microsystem (e) The handwritten rregenerated by the DN (f)After learning the QRS segments of normal (blue inlet) and abnormal (red inlet), the DN generate distinctly dynamics for normal and abnormal ECGs to facilitate data classification.

 

B. Stochastic Neural VLSI for Learning Stochastic Dynamics

Introduction :Many biomedical signals are time-variant and noisy, while the capability to recognise abnormality in such biomedical signals in real-time is crucial for improving the treatmeant on diseases such as epilepsy. The Diffusion Network (DN) has been shown as a probabilistic model capable of modeling noisy, time-variant data as its stochastic dynamics. The analogy between the Diffusion Network and the CRBM further indicates the hardware amenability of the Diffusion Network. This project investigates the capability of the Diffusion Network to model real biomedical data, and its amenability to VLSI implementation.Afterwards, a scalable DN system whose network size can be expanded by interconnecting multiple chips will be designed for general biomedical applications (e.g. data regression at brain-machine interfaces).

 

Characteristics : Matlab or C programming will be used to examine the capability of the Diffusion Network, to simplify the algorithm for VLSI implementation, and to simulate the behaviour of a probabilistic VLSI system based on the Diffusion Network. A prototype of the DN system will then be implemented in VLSI and tested.

Pre-requisites:Experience in Matlab or C programming, Knowledge in Electronics and mixed-mode VLSI design.

Professional skills obtained:Knowledge in probabilistic models, Skills in translating algorithms into VLSI implementation, Methodology in designing VLSI system-on-a-chip.