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.

翻译：随着人类向更高水平的人工智能迈进，计算资源消耗的持续攀升始终是必须付出的代价，这要求我们开发新型解决方案以满足人工智能计算需求的指数级增长。神经形态硬件借鉴了大脑处理信息的方式，有望实现对人工智能工作负载的高效能计算。尽管具备潜力，神经形态硬件尚未在商用人工智能数据中心中得到应用。本文试图分析这一现象的根本原因，并推导出推动神经形态系统实现高效可持续云计算的要求与指导原则：我们首先回顾当前可用的神经形态硬件系统，并收集神经形态解决方案在CPU和GPU上优于传统人工智能处理的案例。接着，我们识别人工智能数据中心中普遍部署的应用、模型与算法，作为神经形态算法研究的未来方向。最后，我们推导出将神经形态系统集成至数据中心所需的硬件与软件要求及最佳实践。通过本文，我们希望提升业界对神经形态硬件融入数据中心所面临挑战的认知，并引导研究社区为实现大规模可持续与高能效的人工智能指明方向。