1,721 to 1,730 of 5,008 Results
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==
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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
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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
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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
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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
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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
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Dec 15, 2023 - Peng Chen
Chen, Peng, 2023, "Related data for: Contention Minimized Bypassing in SMART NoC", https://doi.org/10.21979/N9/BPBOYK, DR-NTU (Data), V1
This dataset contains keycodes for the proposed algorithms in the paper. |
Plain Text - 143 B -
MD5: 1d7103b3e711133d58e43ae1e8c00840
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C++ Source - 1.9 KB -
MD5: 51666e3ad191736f5b4268ba2a86a8c5
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C++ Source - 1.2 KB -
MD5: 645bbe9e036eaddaccf8ab1fab2d3b02
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