An analog non-volatile neural network platform for prototyping RF BIST solutions

Dzmitry Maliuk, Yiorgos Makris
2014 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2014   unpublished
We introduce an analog non-volatile neural network chip which serves as an experimentation platform for prototyping custom classifiers for on-chip integration towards fully standalone built-in self-test (BIST) solutions for RF circuits. Our chip consists of a reconfigurable array of synapses and neurons operating below threshold and featuring sub-μW power consumption. The synapse circuits employ dynamic weight storage for fast bidirectional weight updates during training. The learned weights
more » ... learned weights are then copied onto analog floating gate (FG) memory for permanent storage. The chip architecture supports two learning models: a multilayer perceptron and an ontogenic neural network. A benchmark XOR task is first employed to evaluate the overall learning capability of our chip. The BIST-related effectiveness is then evaluated on two case studies: the detection of parametric and catastrophic faults in an LNA and an RF front-end circuits, respectively.
doi:10.7873/date2014.381 fatcat:xjmsi6whu5f4blzwykiaq4pkoe