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1,621 to 1,630 of 4,781 Results
MAT_Graphs.drawio
Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product
Unknown - 561.9 KB - MD5: 6f6152d299f60c08111cdc90ba83fce7
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
readme.txt
Dec 15, 2023 - Related data for: Contention Minimized Bypassing in SMART NoC
Plain Text - 143 B - MD5: 1d7103b3e711133d58e43ae1e8c00840
Documentation
traffic.cpp
Dec 15, 2023 - Related data for: Contention Minimized Bypassing in SMART NoC
C++ Source - 1.9 KB - MD5: 51666e3ad191736f5b4268ba2a86a8c5
Code
traffic.h
Dec 15, 2023 - Related data for: Contention Minimized Bypassing in SMART NoC
C++ Source - 1.2 KB - MD5: 645bbe9e036eaddaccf8ab1fab2d3b02
Code
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