Cerebras' wafer-scale engine (WSE) technology merges multiple dies on a single wafer. It addresses the challenges of memory bandwidth, latency, and scalability, making it suitable for artificial intelligence. This work evaluates the WSE-3 architecture and compares it with leading GPU-based AI accelerators, notably Nvidia's H100 and B200. The work highlights the advantages of WSE-3 in performance per watt and memory scalability and provides insights into the challenges in manufacturing, thermal management, and reliability. The results suggest that wafer-scale integration can surpass conventional architectures in several metrics, though work is required to address cost-effectiveness and long-term viability.

翻译：Cerebras晶圆级引擎（WSE）技术将多个晶片集成于单一晶圆之上。该技术解决了内存带宽、延迟和可扩展性方面的挑战，使其适用于人工智能领域。本研究评估了WSE-3架构，并将其与主流的基于GPU的AI加速器（特别是英伟达的H100和B200）进行了比较。研究突出了WSE-3在每瓦性能与内存可扩展性方面的优势，并深入探讨了其在制造、热管理和可靠性方面面临的挑战。结果表明，晶圆级集成在多项指标上能够超越传统架构，但在成本效益与长期可行性方面仍需进一步改进。