  This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for
energy-efficient and robust object detection in resource-constrained platforms.
The network architecture is based on Convolutional SNN using
leaky-integrate-fire neuron models. The model combines unsupervised Spike
Time-Dependent Plasticity (STDP) learning with back-propagation (STBP) learning
methods and also uses Monte Carlo Dropout to get an estimate of the uncertainty
error. FSHNN provides better accuracy compared to DNN based object detectors
while being 150X energy-efficient. It also outperforms these object detectors,
when subjected to noisy input data and less labeled training data with a lower
uncertainty error.
