LIU Weichen

Appointment: Nanyang Assistant Professor

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

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121 to 130 of 1,021 Results

MAT_Simulation_Results.tab Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Tabular Data - 1013 B - 8 Variables, 14 Observations - UNF:6:KboaxJ9ZX4FgcAqcXn8SqQ==
optimal.py Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Python Source Code - 5.6 KB - MD5: 4378d4c8895b785248e46a28bb4150b6
README.txt Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Plain Text - 518 B - MD5: 3d58c437be4a1f76fb6a046686cfeb95
README.txt Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Plain Text - 626 B - MD5: 19f17f2b7d19af451d09cc939f60878d
resnet18_PyTorch.pth Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Unknown - 44.7 MB - MD5: e0b1c919e74f9a193d36871d9964bf7d
ste.py Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Python Source Code - 2.0 KB - MD5: 092802c4cc3a3428e1878dd817ecb4b6
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.
artist_states.zip Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks ZIP Archive - 565.8 KB - MD5: a5eb136cfd009eb038607b4c6be8ea4f
FAT Diagrams.drawio Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks Unknown - 60.9 KB - MD5: b328ea1a21afbb917f6a72f394cafe4e
FAT-Mapping-Algo-demo.py Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks Python Source Code - 1.0 KB - MD5: 39f58c4c50c1425208cc8b04bd527420 The data mapping illustration graph

MAT_Simulation_Results.tab

Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product

Tabular Data - 1013 B - 8 Variables, 14 Observations -

optimal.py

Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product

Python Source Code - 5.6 KB -

README.txt

Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product

Plain Text - 518 B -

README.txt

Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product

Plain Text - 626 B -

resnet18_PyTorch.pth

Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product

Unknown - 44.7 MB -

ste.py

Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product

Python Source Code - 2.0 KB -

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.

artist_states.zip

Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks

ZIP Archive - 565.8 KB -

FAT Diagrams.drawio

Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks

Unknown - 60.9 KB -

FAT-Mapping-Algo-demo.py

Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks

Python Source Code - 1.0 KB -

The data mapping illustration graph

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