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3.2 Approximate and Near-Threshold Computing
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pahlevan, Ali Qureshi, Yasir Mahmood Zapater, Marina Bartolini, Andrea Rossi, Davide Benini, Luca Atienza, David |
| Copyright Year | 2020 |
| Abstract | This session focuses on approximate and near-threshold computing. The first paper proposes a novel dynamic virtual machine (VM) allocation method, while guaranteeing quality of service (QoS) requirements. The second paper introduces and presents an adaptive simulation methodology in which neurons in the region of interest (ROI) follow highly accurate biological models while the other neurons follow computation-friendly models. Finally the last paper shows an approximate computing technique to perform approximate computing with memory, avoiding redundant computation when encountering similar input patterns. The session also includes one IP paper on approximate big data computing. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://past.date-conference.com/date18/printpdf/conference/session/3.2 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |