Edge
Computing (EC) is about moving part of the service-specific processing and data
storage from the Cloud Computing to the edge network nodes. Among the expected
benefits of EC deployment in 5G, there are: performance improvements, traffic
optimization and new ultra-low-latency services.
If today EC is getting
momentum, we’re witnessing, at the same time, a growing development of
Artificial Intelligence (AI) for a wide spectrum of applications, such as: intelligent
personal assistants, video/audio surveillance, smart cities’ applications, self-driving,
Industry 4.0. The requirements of these applications seem calling an AI’s resources-hungry
model, whose cloud-centric execution appears in the opposite direction with a
migration of computing, storage and networking resources at the edge.
In
reality, the two technology trends are crossing in the Edge intelligence (EI):
an emerging paradigm meeting the challenging requirements of future pervasive
services scenarios where optical-radio networks requires automatic real-time
joint optimization of heterogeneous computation, communication, and
memory/cache resources and high dimensional fast configurations (e.g.,
selecting and combining optimum network functions and inference techniques).
Moreover, the nexus of EI with distributed ledger technologies will enable new
collaborative ecosystems which can include, but are not limited to: network
operators, platform providers, AI technology/software providers and Users.
A major
roadblock to this vision is the long-term extrapolations of the energy
consumption needs of a pervasive Artificial Intelligence embedded into future
network infrastructures.
Low-latency and low-energy neural network computations
can be a game changer. In this direction, fully optical neural network could
offer impressive enhancements in computational speed and reduced power
consumptions.