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Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
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| Published in: | ArXiv cs.AR Recent Papers |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1865127356486647809 |
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| collection | WordPress RSS FRELIP Feed Integration |
| container_title | ArXiv cs.AR Recent Papers |
| description | |
| discipline_display | Engineering & Technology |
| discipline_facet | Engineering & Technology |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:51521 |
| institution | FRELIP |
| journal_source_facet | ArXiv cs.AR Recent Papers |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications ArXiv cs.AR Recent Papers Chemical Engineering Engineering & Technology |
| sub_discipline_display | Chemical Engineering |
| sub_discipline_facet | Chemical Engineering |
| subject_display | ArXiv cs.AR Recent Papers Chemical Engineering Engineering & Technology ArXiv cs.AR Recent Papers Chemical Engineering Engineering & Technology |
| subject_facet | ArXiv cs.AR Recent Papers Chemical Engineering Engineering & Technology |
| title | Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications |
| title_auth | Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications |
| title_full | Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications |
| title_fullStr | Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications |
| title_full_unstemmed | Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications |
| title_short | Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications |
| title_sort | improving the performance and learning stability of parallelizable rnns designed for ultra-low power applications |
| topic | ArXiv cs.AR Recent Papers Chemical Engineering Engineering & Technology |
| url | https://arxiv.org/abs/2605.11855v1 |