  Temporal Neural Networks (TNNs) are spiking neural networks that exhibit
brain-like sensory processing with high energy efficiency. This work presents
the ongoing research towards developing a custom design framework for designing
efficient application-specific TNN-based Neuromorphic Sensory Processing Units
(NSPUs). This paper examines previous works on NSPU designs for UCR time-series
clustering and MNIST image classification applications. Current ideas for a
custom design framework and tools that enable efficient software-to-hardware
design flow for rapid design space exploration of application-specific NSPUs
while leveraging EDA tools to obtain post-layout netlist and
power-performance-area (PPA) metrics are described. Future research directions
are also outlined.
