Hardware-based Artificial Neural Networks for Size, Weight, and Power Constrained Platforms (Preprint)

A fully parallel, silicon-based artificial neural network (CogniMem CM1K) built on zero instruction set computer technology was used for change detection and object identification in video data. Fundamental pattern recognition capabilities were demonstrated with reduced neuron numbers utilizing only a few, or in some cases one, neuron per category. This simplified approach was used to validate the utility of few neuron networks for use in applications that necessitate severe size, weight, and power restrictions. The limited resource requirements and massively parallel nature of hardware-based artificial neural networks make them superior to many software approaches in resource limited systems, such as micro-UAVs, mobile sensor platforms, and pocket-sized robots. Submitted to IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), Singapore, 15-19 April 2013.