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DR-NTU (Data) is for research data deposit. For research paper deposits, please use DR-NTU.

Mission: DR-NTU (Data) curates, stores, preserves, makes available and enables the download of digital data generated by the NTU research community. The repository develops and provides guidance for managing, sharing, and reusing research data to promote responsible data sharing in support of open science and research integrity.

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What can be deposited? Final, non-sensitive research data from projects carried out at NTU. The uploaded content must not infringe upon the copyrights or other intellectual property rights, and must be void of all identifiable information.

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1,001 to 1,010 of 2,486 Results
Replication Data for: Reduced Worst-Case Communication Latency Using Single-Cycle Multi-Hop Traversal Network-on-Chip
Dec 14, 2023 - Peng Chen
Chen, Peng, 2023, "Replication Data for: Reduced Worst-Case Communication Latency Using Single-Cycle Multi-Hop Traversal Network-on-Chip", https://doi.org/10.21979/N9/WIQKUF, DR-NTU (Data), V1
This dataset contains the keycodes of this paper.
Related data for: Lightweight Thermal Monitoring in Optical Networks-on-Chip via Router Reuse
Dec 14, 2023 - Li Mengquan
Li, Mengquan; Liu, Weichen, 2023, "Related data for: Lightweight Thermal Monitoring in Optical Networks-on-Chip via Router Reuse", https://doi.org/10.21979/N9/O2VA8M, DR-NTU (Data), V1
Conference paper: “Mengquan Li, Jun Zhou, and Weichen Liu, "Lightweight Thermal Monitoring in Optical Networks-on-Chip via Router Reuse” in Design, Automation and Test in Europe Conference (DATE), 2020.
Related data for: Contention-aware Routing for Thermal-Reliable Optical Networks-on-Chip
Dec 14, 2023 - Li Mengquan
Li, Mengquan; Liu, Weichen, 2023, "Related data for: Contention-aware Routing for Thermal-Reliable Optical Networks-on-Chip", https://doi.org/10.21979/N9/FUJ0OE, DR-NTU (Data), V1
Journal paper: Mengquan Li, Weichen Liu, Luan H.K. Duong, Peng Chen, and Lei Yang, “Contention-aware Routing for Thermal-Reliable Optical Networks-on-Chip”, accepted in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020
Related data for: DATE2019-Thermal sensing using micro-ring resonators in optical network-on-chip
Dec 14, 2023 - Li Mengquan
Li, Mengquan; Liu, Weichen, 2023, "Related data for: DATE2019-Thermal sensing using micro-ring resonators in optical network-on-chip", https://doi.org/10.21979/N9/2Q2QXP, DR-NTU (Data), V1
In this paper, we for the first time utilize the micro-ring resonators (MRs) in optical networks-on-chip (ONoCs) to implement thermal sensing without requiring additional hardware or chip area. The challenges in accuracy and reliability that arise from fabrication-induced process...
Related data for: TECS2019-Hardware-Software Collaborative Thermal Sensing in Optical Network-on-Chip Based Manycore Systems
Dec 14, 2023 - Li Mengquan
Li, Mengquan; Liu, Weichen, 2023, "Related data for: TECS2019-Hardware-Software Collaborative Thermal Sensing in Optical Network-on-Chip Based Manycore Systems", https://doi.org/10.21979/N9/9EKPP2, DR-NTU (Data), V1
Continuous technology scaling in manycore systems leads to severe overheating issues. To guarantee system reliability, it is critical to accurately yet efficiently monitor runtime temperature distribution for effective chip thermal management. As an emerging communication archite...
Replication Data for: HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs
Replication Data for: HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs
Dec 14, 2023 - KONG Hao
Kong, Hao; Liu, Weichen, 2023, "Replication Data for: HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs", https://doi.org/10.21979/N9/KSXK4T, DR-NTU (Data), V1
This is the source code repository for the publication "HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs".
Replication Data for: EDLAB: A Benchmark for Edge Deep Learning Accelerators
Replication Data for: EDLAB: A Benchmark for Edge Deep Learning Accelerators
Dec 14, 2023 - KONG Hao
Kong, Hao; Liu, Weichen, 2023, "Replication Data for: EDLAB: A Benchmark for Edge Deep Learning Accelerators", https://doi.org/10.21979/N9/NT2HXS, DR-NTU (Data), V1, UNF:6:vfraJpp92Ek2OdfPAVmxeg== [fileUNF]
This is the program source code for the publication "EDLAB: A Benchmark for Edge Deep Learning Accelerators"
Replication Data for: Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware
Dec 14, 2023 - KONG Hao
Kong, Hao; Liu, Weichen, 2023, "Replication Data for: Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware", https://doi.org/10.21979/N9/NB6FU2, DR-NTU (Data), V1
This dataset is created to store the related data of the following published paper: Hao Kong, Di Liu, Shuo Huai, Xiangzhong Luo, Weichen Liu*, Ravi Subramaniam, Christian Makaya, Qian Lin, “Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hard...
Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI
Dec 14, 2023 - KONG Hao
Kong, Hao; Liu, Weichen, 2023, "Replication Data for: EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI", https://doi.org/10.21979/N9/HGFYTJ, DR-NTU (Data), V1, UNF:6:YowCvL3Bn3yfomoWb2+WPg== [fileUNF]
This dataset is created to restore the related data of the following published paper: Hao Kong, Xiangzhong Luo, Shuo Huai, Di Liu, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu*, “EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI”, ACM/IEEE De...
Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning
Dec 14, 2023 - KONG Hao
Kong, Hao; Liu, Weichen, 2023, "Replication Data for: Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning", https://doi.org/10.21979/N9/BTNOJN, DR-NTU (Data), V1, UNF:6:YowCvL3Bn3yfomoWb2+WPg== [fileUNF]
This dataset is created to store the related data of the following published paper: Hao Kong, Di Liu, Xiangzhong Luo, Shuo Huai, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu*, “Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional...
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