Zhu Shien

Appointment: PhD student

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.

21 to 30 of 189 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; 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; 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

Add Data

Share Dataverse

Link Dataverse

Reset Modifications