LIU Weichen

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

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1 to 10 of 35 Results

Other Projects of the Liu Team(Nanyang Technological University) Apr 9, 2026
Replication Data for: Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems Apr 7, 2026 - Luo Xiangzhong Luo, Xiangzhong; Liu, Weichen, 2026, "Replication Data for: Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems", https://doi.org/10.21979/N9/VVAKSR, DR-NTU (Data), V1 The dataset includes the source codes and readme file for implementing the design presented in the paper "Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems", accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and S...
Replication Data for: Double-Win NAS: Towards Deep-to-Shallow Transformable Neural Architecture Search for Intelligent Embedded Systems Apr 7, 2026 - Luo Xiangzhong Luo, Xiangzhong; Liu, Di; Kong, Hao; Huai, Shuo; Liu, Weichen, 2026, "Replication Data for: Double-Win NAS: Towards Deep-to-Shallow Transformable Neural Architecture Search for Intelligent Embedded Systems", https://doi.org/10.21979/N9/BIOGHZ, DR-NTU (Data), V1 The dataset includes the source codes and readme file for implementing the design presented in the paper "Double-Win NAS: Towards Deep-to-Shallow Transformable Neural Architecture Search for Intelligent Embedded Systems"
Replication Data for: Simulating CXL Shared Coherent Memory Using Shared Memory Among Virtual Machines Mar 13, 2026 - Other Projects of the Liu Team Wu, Ting; Long, Linbo; Ma, Zhulin; Yu, Qiushuang; Tian, Hang; Liu, Weichen, 2026, "Replication Data for: Simulating CXL Shared Coherent Memory Using Shared Memory Among Virtual Machines", https://doi.org/10.21979/N9/BUO9DY, DR-NTU (Data), V1 The dataset includes the source codes and readme file for implementing the design presented in the paper "Simulating CXL Shared Coherent Memory Using Shared Memory Among Virtual Machines"
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 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 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 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 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"

Other Projects of the Liu Team(Nanyang Technological University)

Apr 9, 2026

Replication Data for: Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems

Apr 7, 2026 - Luo Xiangzhong

Luo, Xiangzhong; Liu, Weichen, 2026, "Replication Data for: Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems", https://doi.org/10.21979/N9/VVAKSR, DR-NTU (Data), V1

The dataset includes the source codes and readme file for implementing the design presented in the paper "Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems", accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and S...

Replication Data for: Double-Win NAS: Towards Deep-to-Shallow Transformable Neural Architecture Search for Intelligent Embedded Systems

Apr 7, 2026 - Luo Xiangzhong

Luo, Xiangzhong; Liu, Di; Kong, Hao; Huai, Shuo; Liu, Weichen, 2026, "Replication Data for: Double-Win NAS: Towards Deep-to-Shallow Transformable Neural Architecture Search for Intelligent Embedded Systems", https://doi.org/10.21979/N9/BIOGHZ, DR-NTU (Data), V1

The dataset includes the source codes and readme file for implementing the design presented in the paper "Double-Win NAS: Towards Deep-to-Shallow Transformable Neural Architecture Search for Intelligent Embedded Systems"

Replication Data for: Simulating CXL Shared Coherent Memory Using Shared Memory Among Virtual Machines

Mar 13, 2026 - Other Projects of the Liu Team

Wu, Ting; Long, Linbo; Ma, Zhulin; Yu, Qiushuang; Tian, Hang; Liu, Weichen, 2026, "Replication Data for: Simulating CXL Shared Coherent Memory Using Shared Memory Among Virtual Machines", https://doi.org/10.21979/N9/BUO9DY, DR-NTU (Data), V1

The dataset includes the source codes and readme file for implementing the design presented in the paper "Simulating CXL Shared Coherent Memory Using Shared Memory Among Virtual Machines"

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

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

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

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

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"

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