Please use this identifier to cite or link to this item:
http://111.93.178.142:25000/jspui/handle/123456789/7842Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Manmohan Brahma | - |
| dc.date.accessioned | 2026-10-07T12:29:40Z | - |
| dc.date.available | 2026-10-07T12:29:40Z | - |
| dc.date.issued | 2026-10-07 | - |
| dc.identifier.uri | http://111.93.178.142:25000/jspui/handle/123456789/7842 | - |
| dc.description | Vol. 131, No.1, p 64 | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Distributed training optimisation | en_US |
| dc.subject | edge AI and NPU workloads | en_US |
| dc.subject | high bandwidth memory | en_US |
| dc.subject | key value cache management | en_US |
| dc.subject | large languge model deployment | en_US |
| dc.title | Memory hierarchy in AI systems : dual contexts, estimation modelling and optimisation strategies | en_US |
| dc.type | Other | en_US |
| Appears in Collections: | Vol. 131, No.1, 2026 | |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.