Exploring emerging CNFET for efficient last level cache design

Author:

Xu Dawen1,Li Li1,Wang Ying2,Liu Cheng2,Li Huawei2

Affiliation:

1. Institute of Computing Technology Chinese Academy of Science, Beijing, China and School of Electronic Science & Applied Physics Hefei University of Technology Anhui, China

2. Institute of Computing Technology Chinese Academy of Science, Beijing, China

Publisher

ACM

Reference20 articles.

1. Song L Wang Y Han Y etal C-brain:a deep learning accelerator that tames the diversity of CNNs through adaptive data-level parallelization{J}. 2016:1--6. 10.1145/2897937.2897995 Song L Wang Y Han Y et al. C-brain:a deep learning accelerator that tames the diversity of CNNs through adaptive data-level parallelization{J}. 2016:1--6. 10.1145/2897937.2897995

2. DeepBurning

3. A survey on cache tuning from a power/energy perspective

4. Digital VLSI logic technology using Carbon Nanotube FETs

5. Carbon nanotube circuits in the presence of carbon nanotube density variations

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Taming Process Variations in CNFET for Efficient Last-Level Cache Design;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2021

2. Carbon Nanotube Length Variation in Correlated CNFETs;2020 IEEE 20th International Conference on Nanotechnology (IEEE-NANO);2020-07

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