A System Level Performance Evaluation for Superconducting Digital Systems
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917836050399232 |
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| author | Kundu, Joyjit Bhattacharjee, Debjyoti Josephsen, Nathan Pokhrel, Ankit De Silva, Udara Guo, Wenzhe Van Winckel, Steven Brebels, Steven Perumkunnil, Manu Herr, Quentin Herr, Anna |
| author_facet | Kundu, Joyjit Bhattacharjee, Debjyoti Josephsen, Nathan Pokhrel, Ankit De Silva, Udara Guo, Wenzhe Van Winckel, Steven Brebels, Steven Perumkunnil, Manu Herr, Quentin Herr, Anna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A System Level Performance Evaluation for Superconducting Digital Systems Kundu, Joyjit Bhattacharjee, Debjyoti Josephsen, Nathan Pokhrel, Ankit De Silva, Udara Guo, Wenzhe Van Winckel, Steven Brebels, Steven Perumkunnil, Manu Herr, Quentin Herr, Anna Hardware Architecture Artificial Intelligence Emerging Technologies 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. |
| title | A System Level Performance Evaluation for Superconducting Digital Systems |
| topic | Hardware Architecture Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2411.08645 |