A case study for the implementation of a stochastic bit stream neuron; the choice between electrical and optical interconnects

A neural network architecture is described which uses stochastic processing techniques to perform the weighted input multiplication, summation and thresholding processes of a neuron using the optimal amount of hardware. It is argued that the advantage of this approach is that it will allow large neural networks to be fabricated with relatively small amounts of hardware. The architecture allows a choice to be made between the speed and accuracy of processing, as well as a choice of hardware. Implementations of a bit stream neuron using electronic, optoelectronic and optical hardware are developed and their capabilities are compared based on speed of processing and network size. The aim of the study is to investigate the capabilities of optical logic in distributed processing systems, together with the use of the optical thyristor as logic elements. It is shown that optical processing and optical interconnection allows a simplification of the processing sequence and allows the parallelism of distributed systems to be utilised