LIU Weichen

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

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191 to 200 of 1,095 Results

tools.cpython-37.pyc Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting Unknown - 3.4 KB - MD5: 33a3575c1f94cd46bcace269dd627f2e
tools.py Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting Python Source Code - 3.3 KB - MD5: dd31ee362385191999b4c9842b57628a
train.py Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting Python Source Code - 13.8 KB - MD5: 8ad9469b460a8094d2ca9e2182d5585b
valprep.sh Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting Shell Script - 2.1 MB - MD5: 25267e7acda11b174c9d452368cec92e
artist_states.zip Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product ZIP Archive - 392.6 KB - MD5: 730560a1643f448d608dc0931f29df03
bashrc Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product Plain Text - 953 B - MD5: 0db0ea9b723110c520c123c29370f4ae
Code.zip Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product ZIP Archive - 1.2 MB - MD5: e7a9da0c99c849019bea1ab4a8f1aa6c
DPU_complex_Vdd12_Clk04.png Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product PNG Image - 1.5 MB - MD5: 4581c4f9b45ece7ed74bbc9eb1b3f7c4
DPU_Simple_02n.png Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product PNG Image - 156.3 KB - MD5: 11dd6541d3174d3982cdf92fc62330b1
FreePDK45.zip Dec 18, 2023 - Replication Data for: iMAT: Energy-Efficient In-Memory Acceleration of Ternary Neural Networks With Sparse Dot Product ZIP Archive - 3.0 MB - MD5: 5d6f549cc260b2b8bd6fecb65a1b31d9

tools.cpython-37.pyc

Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting

Unknown - 3.4 KB -

tools.py

Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting

Python Source Code - 3.3 KB -

train.py

Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting

Python Source Code - 13.8 KB -

valprep.sh

Dec 18, 2023 - Related Data for: Pearls Hide Behind Linearity: Simplifying Deep Convolutional Networks for Embedded Hardware Systems via Linearity Grafting

Shell Script - 2.1 MB -