College of Computing and Data Science (CCDS)

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1,761 to 1,770 of 4,915 Results

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
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
FAT-test1-2021-8-14.png Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks PNG Image - 1.4 MB - MD5: 6ee327c849a8f93c730f86a5744c9f8f
FAT-test1-In30p-Out.png Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks PNG Image - 1.1 MB - MD5: bab45fa831f415639dc23ff7c9e1da9f
FAT-test2-2021-8-14.png Dec 14, 2023 - Replication Data for: FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks PNG Image - 1.4 MB - MD5: 7b807e55e22e0c07b0bebc8894c11deb

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 -

readme.txt

Dec 15, 2023 - Related data for: Contention Minimized Bypassing in SMART NoC

Plain Text - 143 B -

Documentation

traffic.cpp

Dec 15, 2023 - Related data for: Contention Minimized Bypassing in SMART NoC

C++ Source - 1.9 KB -

Code

traffic.h