Superconducting Digital (SCD) technology offers significant potential for enhancing the performance of next generation large scale compute workloads. By leveraging advanced lithography and a 300 mm platform, SCD devices can reduce energy consumption and boost computational power. This paper presents a cross-layer modeling approach to evaluate the system-level performance benefits of SCD architectures for Large Language Model (LLM) training and inference. Our findings, based on experimental data and Pulse Conserving Logic (PCL) design principles, demonstrate substantial performance gain in both training and inference. We are, thus, able to convincingly show that the SCD technology can address memory and interconnect limitations of present day solutions for next-generation compute systems.

翻译：超导数字（SCD）技术为提升下一代大规模计算工作负载的性能提供了显著潜力。通过采用先进光刻技术与300毫米平台，SCD器件能够降低能耗并提升计算能力。本文提出一种跨层建模方法，用于评估SCD架构在大型语言模型（LLM）训练与推理中的系统级性能优势。基于实验数据与脉冲守恒逻辑（PCL）设计原理，我们的研究结果证明了该技术在训练与推理任务中均能带来显著的性能提升。由此，我们能够有力证明SCD技术能够解决下一代计算系统中现有方案在内存与互连方面的局限性。