Research data from Liu Weichen's team.

Appointment: Nanyang Assistant Professor

Research topics:
• Embedded and Real-Time Systems
• Many-Core Systems
• Electronic/Photonic Network-on-Chip
• Machine Learning Acceleration
• Computer Architecture

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

1 to 10 of 31 Results
Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting
Dec 18, 2023 - Luo Xiangzhong
Luo, Xiangzhong; Liu, Weichen, 2023, "Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting", https://doi.org/10.21979/N9/SJX3HR, DR-NTU (Data), V1
The related data for "Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting".
Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product
Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product
Dec 18, 2023 - Zhu Shien
Zhu, Shien; Huai, Shuo; Xiong, Guochu; Liu, Weichen, 2023, "Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product", https://doi.org/10.21979/N9/B4SIIN, DR-NTU (Data), V1, UNF:6:KboaxJ9ZX4FgcAqcXn8SqQ== [fileUNF]
This dataset contains the simulation environment, simulation results, and post-processing data for the iMAT paper published in ISLPED 2023.
Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks
Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks
Dec 14, 2023 - Zhu Shien
Zhu, Shien; Duong, H. K. Luan; Chen, Hui; Di, Liu; Liu, Weichen, 2023, "Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks", https://doi.org/10.21979/N9/DYKUPV, DR-NTU (Data), V1, UNF:6:UCCl9ySs+IN5trVkl2AGQQ== [fileUNF]
This dataset contains the codes, figures, and tables for the TCAD 2022 paper: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks.
Replication Data for: EdgeCompress: Coupling Multi-Dimensional Model Compression and Dynamic Inference for EdgeAI
Dec 14, 2023 - Kong Hao
Kong, Hao; Liu, Weichen, 2023, "Replication Data for: EdgeCompress: Coupling Multi-Dimensional Model Compression and Dynamic Inference for EdgeAI", https://doi.org/10.21979/N9/GCAMZH, DR-NTU (Data), V1
This is the source code repository for the publication "EdgeCompress: Coupling Multi-Dimensional Model Compression and Dynamic Inference for EdgeAI".
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...
Kong Hao(Nanyang Technological University)
Dec 14, 2023
Appointment: PhD Student
Add Data

Log in to create a dataverse or add a dataset.

Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.