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 37 Results
SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention
Jun 5, 2026 - Other Projects of the Liu Team
Zihan, Teng; Jiayu, Zhao; Wentao, Ren; Minhao, Fan; Tianrui, Ma; Song, Chen; Weichen, Liu, 2026, "SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention", https://doi.org/10.21979/N9/EKDD1U, DR-NTU (Data), V1
The dataset includes the source codes and readme file for implementing the design presented in the paper "SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention"
Related Data for: BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
Jun 5, 2026 - Other Projects of the Liu Team
Jiayu, Zhao; Zihan, Teng; Minhao, Fan; Tianrui, Ma; Wentao, Ren; Song, Chen; Weichen, Liu, 2026, "Related Data for: BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization", https://doi.org/10.21979/N9/R7OS8U, DR-NTU (Data), V1
The dataset includes the source codes and readme file for implementing the design presented in the paper "BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization"
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
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".
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