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，计算资源消耗不断攀升，亟需开发创新解决方案来满足AI计算需求的指数级增长。神经形态硬件受大脑信息处理方式的启发，有望实现AI工作负载的节能计算。尽管潜力巨大，但神经形态硬件尚未进入商用AI数据中心。本文尝试分析其根本原因，并推导出推动神经形态系统实现高效可持续云计算的必要条件和指导原则：首先，我们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常规AI处理中超越CPU和GPU的案例。其次，我们识别AI数据中心常用部署的应用程序、模型和算法，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到数据中心所需的软硬件要求与最佳实践。通过本文，我们希望能提高业界对神经形态硬件融入数据中心挑战的认识，并引导学术界实现大规模可持续、高能效的AI。