超导数字技术可显著提升大模型训练推理性能。
A System Level Performance Evaluation for Superconducting Digital Systems
- 跨层建模评估超导芯片在大模型中的表现。
- 实验与脉冲保持逻辑设计表明训练推理性能大幅提升。
- 适合关注下一代计算系统能效与算力突破的研究者。
超导数字（SCD）技术有望显著提升下一代大规模计算任务的性能。通过采用先进光刻工艺和300 mm制造平台，SCD器件可降低能耗并提升计算能力。本文提出一种跨层建模方法，评估SCD架构在大语言模型（LLM）训练与推理中的系统级性能优势。基于实验数据及脉冲保持逻辑（PCL）设计原则，研究结果表明，SCD在训练与推理阶段均实现显著性能提升。因此，我们有力证明了该技术能够解决当前计算系统中内存与互连的瓶颈问题，为下一代计算系统提供可行方案。
原文摘要 · Abstract (English)
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.
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