  With the rapid development of artificial intelligence in recent years,
mankind is facing an unprecedented demand for data processing. Today, almost
all data processing is performed using electrons in conventional complementary
metal-oxide-semiconductor (CMOS) circuits. Over the past few decades,
scientists have been searching for faster and more efficient ways to process
data. Now, magnons, the quanta of spin waves, show the potential for higher
efficiency and lower energy consumption in solving some specific problems.
While magnonics remains predominantly in the realm of academia, significant
efforts are being made to explore the scientific and technological challenges
of the field. Numerous proof-of-concept prototypes have already been
successfully developed and tested in laboratories. In this article, we review
the developed magnonic devices and discuss the current challenges in realizing
magnonic circuits based on these building blocks. We look at the application of
spin waves in neuromorphic networks, stochastic and reservoir computing and
discuss the advantages over conventional electronics in these areas. We then
introduce a new powerful tool, inverse design magnonics, which has the
potential to revolutionize the field by enabling the precise design and
optimization of magnonic devices in a short time. Finally, we provide a
theoretical prediction of energy consumption and propose benchmarks for
universal magnonic circuits.
