schedule
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schedule [2022/01/07 14:22] – rahbera | schedule [2022/01/14 12:47] (current) – mnika | ||
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Performance & QoS Wrap-Up </ | Performance & QoS Wrap-Up </ | ||
| ::: | ::: | | <hi #fff200> L14b: Emerging Memory Technologies </ | | ::: | ::: | | <hi #fff200> L14b: Emerging Memory Technologies </ | ||
- | | W8 | 18.11 \\ Thu. | {{youtube> | + | | W8 | 18.11 \\ Thu. | {{youtube> |
- | | ::: | ::: | ::: | <hi #fff200> L15b: Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks </hi> \\ {{mensa__comparch__fall2021.pdf|(PDF)}} {{mensa__comparch__fall2021.pptx|(PPT)}} {{https:// | + | | ::: | ::: | ::: | <hi #fff200> L15b: Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks </hi> \\ {{mensa__comparch__fall2021.pdf|(PDF)}} {{mensa__comparch__fall2021.pptx|(PPT)}} {{https:// |
- | | ::: | ::: | ::: | <hi #fff200> L15c: CODIC: A Low-Cost Substrate for Enabling Custom In-DRAM Functionalities and Optimizations </hi> \\ {{codic-full-v1.1.pdf|(PDF)}} {{codic-full-v1.1.pptx|(PPT)}} {{https:// | + | | ::: | ::: | ::: | <hi #fff200> L15c: CODIC: A Low-Cost Substrate for Enabling Custom In-DRAM Functionalities and Optimizations </hi> \\ {{codic-full-v1.1.pdf|(PDF)}} {{codic-full-v1.1.pptx|(PPT)}} {{https:// |
| ::: | 19.11 \\ Fri. | {{youtube> | | ::: | 19.11 \\ Fri. | {{youtube> | ||
| ::: | ::: | ::: | <hi #fff200> L16b: A Deeper Look into RowHammer’s Sensitivities: | | ::: | ::: | ::: | <hi #fff200> L16b: A Deeper Look into RowHammer’s Sensitivities: |
schedule.txt · Last modified: 2022/01/14 12:47 by mnika