  The increasing rise in machine learning and deep learning applications is
requiring ever more computational resources to successfully meet the growing
demands of an always-connected, automated world. Neuromorphic technologies
based on Spiking Neural Network algorithms hold the promise to implement
advanced artificial intelligence using a fraction of the computations and power
requirements by modeling the functioning, and spiking, of the human brain. With
the proliferation of tools and platforms aiding data scientists and machine
learning engineers to develop the latest innovations in artificial and deep
neural networks, a transition to a new paradigm will require building from the
current well-established foundations. This paper explains the theoretical
workings of neuromorphic technologies based on spikes, and overviews the
state-of-art in hardware processors, software platforms and neuromorphic
sensing devices. A progression path is paved for current machine learning
specialists to update their skillset, as well as classification or predictive
models from the current generation of deep neural networks to SNNs. This can be
achieved by leveraging existing, specialized hardware in the form of SpiNNaker
and the Nengo migration toolkit. First-hand, experimental results of converting
a VGG-16 neural network to an SNN are shared. A forward gaze into industrial,
medical and commercial applications that can readily benefit from SNNs wraps up
this investigation into the neuromorphic computing future.
