  Spiking neural networks (SNNs) recently gained momentum due to their
low-power multiplication-free computing and the closer resemblance of
biological processes in the nervous system of humans. However, SNNs require
very long spike trains (up to 1000) to reach an accuracy similar to their
artificial neural network (ANN) counterparts for large models, which offsets
efficiency and inhibits its application to low-power systems for real-world use
cases. To alleviate this problem, emerging neural encoding schemes are proposed
to shorten the spike train while maintaining the high accuracy. However,
current accelerators for SNN cannot well support the emerging encoding schemes.
In this work, we present a novel hardware architecture that can efficiently
support SNN with emerging neural encoding. Our implementation features energy
and area efficient processing units with increased parallelism and reduced
memory accesses. We verified the accelerator on FPGA and achieve 25% and 90%
improvement over previous work in power consumption and latency, respectively.
At the same time, high area efficiency allows us to scale for large neural
network models. To the best of our knowledge, this is the first work to deploy
the large neural network model VGG on physical FPGA-based neuromorphic
hardware.
