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RNGD: A 5nm Tensor-Contraction Processor for Power-Efficient Inference on Large Language Models
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 한기진 | - |
| dc.date.accessioned | 2026-01-17T00:30:36Z | - |
| dc.date.available | 2026-01-17T00:30:36Z | - |
| dc.date.issued | 2025-02-17 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/63162 | - |
| dc.title | RNGD: A 5nm Tensor-Contraction Processor for Power-Efficient Inference on Large Language Models | - |
| dc.type | Conference | - |
| dc.citation.startPage | 284 | - |
| dc.citation.endPage | 286 | - |
| dc.citation.conferenceName | 2025 IEEE International Solid-State Circuits Conference (ISSCC) | - |
| dc.citation.conferencePlace | 미국 | - |
| dc.citation.conferenceDate | 2025-02-16 ~ 2025-02-20 | - |
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Related Researcher
- Han, Ki Jin
- College of Engineering (Department of Electronics and Electrical Engineering)