  As humans advance toward a higher level of artificial intelligence, it is
always at the cost of escalating computational resource consumption, which
requires developing novel solutions to meet the exponential growth of AI
computing demand. Neuromorphic hardware takes inspiration from how the brain
processes information and promises energy-efficient computing of AI workloads.
Despite its potential, neuromorphic hardware has not found its way into
commercial AI data centers. In this article, we try to analyze the underlying
reasons for this and derive requirements and guidelines to promote neuromorphic
systems for efficient and sustainable cloud computing: We first review
currently available neuromorphic hardware systems and collect examples where
neuromorphic solutions excel conventional AI processing on CPUs and GPUs. Next,
we identify applications, models and algorithms which are commonly deployed in
AI data centers as further directions for neuromorphic algorithms research.
Last, we derive requirements and best practices for the hardware and software
integration of neuromorphic systems into data centers. With this article, we
hope to increase awareness of the challenges of integrating neuromorphic
hardware into data centers and to guide the community to enable sustainable and
energy-efficient AI at scale.
