  One of the most exciting advancements in AI over the last decade is the wide
adoption of ANNs, such as DNN and CNN, in many real-world applications.
However, the underlying massive amounts of computation and storage requirement
greatly challenge their applicability in resource-limited platforms like the
drone, mobile phone, and IoT devices etc. The third generation of neural
network model--Spiking Neural Network (SNN), inspired by the working mechanism
and efficiency of human brain, has emerged as a promising solution for
achieving more impressive computing and power efficiency within light-weighted
devices (e.g. single chip). However, the relevant research activities have been
narrowly carried out on conventional rate-based spiking system designs for
fulfilling the practical cognitive tasks, underestimating SNN's energy
efficiency, throughput, and system flexibility. Although the time-based SNN can
be more attractive conceptually, its potentials are not unleashed in realistic
applications due to lack of efficient coding and practical learning schemes. In
this work, a Precise-Time-Dependent Single Spike Neuromorphic Architecture,
namely "PT-Spike", is developed to bridge this gap. Three constituent
hardware-favorable techniques: precise single-spike temporal encoding,
efficient supervised temporal learning, and fast asymmetric decoding are
proposed accordingly to boost the energy efficiency and data processing
capability of the time-based SNN at a more compact neural network model size
when executing real cognitive tasks. Simulation results show that "PT-Spike"
demonstrates significant improvements in network size, processing efficiency
and power consumption with marginal classification accuracy degradation when
compared with the rate-based SNN and ANN under the similar network
configuration.
