Neuromorphic hardware for sustainable AI data centers

TL;DR

This paper analyzes the potential of neuromorphic hardware to enable energy-efficient AI data centers, reviewing current systems, identifying suitable applications, and proposing integration guidelines for sustainable AI infrastructure.

Contribution

It provides a comprehensive review of neuromorphic hardware, identifies key applications for data centers, and offers requirements and best practices for integration into sustainable AI systems.

Findings

Neuromorphic hardware can outperform CPUs and GPUs in specific AI tasks.

Current neuromorphic systems are not yet widely adopted in commercial data centers.

Guidelines for hardware-software integration to promote sustainable AI are proposed.

Abstract

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.