PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning
  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.
