  While classical neural networks take a position of a leading method in the
machine learning community, spiking neuromorphic systems bring attention and
large projects in neuroscience. Spiking neural networks were shown to be able
to substitute networks of classical neurons in applied tasks. This work
explores recent hardware designs focusing on perspective applications (like
convolutional neural networks) for both neuron types from the energy efficiency
side to analyse whether there is a possibility for spiking neuromorphic
hardware to grow up for a wider use. Our comparison shows that spiking hardware
is at least on the same level of energy efficiency or even higher than
non-spiking on a level of basic operations. However, on a system level, spiking
systems are outmatched and consume much more energy due to inefficient data
representation with a long series of spikes. If spike-driven applications,
minimizing an amount of spikes, are developed, spiking neural systems may reach
the energy efficiency level of classical neural systems. However, in the near
future, both type of neuromorphic systems may benefit from emerging memory
technologies, minimizing the energy consumption of computation and memory for
both neuron types. That would make infrastructure and data transfer energy
dominant on the system level. We expect that spiking neurons have some
benefits, which would allow achieving better energy results. Still the problem
of an amount of spikes will still be the major bottleneck for spiking hardware
systems.
