  The explosion of artificial intelligence and machine-learning algorithms,
connected to the exponential growth of the exchanged data, is driving a search
for novel application-specific hardware accelerators. Among the many, the
photonics field appears to be in the perfect spotlight for this global data
explosion, thanks to its almost infinite bandwidth capacity associated with
limited energy consumption. In this review, we will overview the major
advantages that photonics has over electronics for hardware accelerators,
followed by a comparison between the major architectures implemented on
Photonics Integrated Circuits (PIC) for both the linear and nonlinear parts of
Neural Networks. By the end, we will highlight the main driving forces for the
next generation of photonic accelerators, as well as the main limits that must
be overcome.
