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Replication Data for: iMAD: An In-Memory Accelerator for AdderNet with Efficient 8-bit Addition and Subtraction Operations
Replication Data for: iMAD: An In-Memory Accelerator for AdderNet with Efficient 8-bit Addition and Subtraction Operations
May 12, 2022
Zhu, Shien; Li, Shiqing; Liu, Weichen, 2022, "Replication Data for: iMAD: An In-Memory Accelerator for AdderNet with Efficient 8-bit Addition and Subtraction Operations", https://doi.org/10.21979/N9/JNFW9P, DR-NTU (Data), V2, UNF:6:L3+xW/BxBE6fYNWBm4ta6Q== [fileUNF]
The simulation code, experiment results, and graphs used in the GLSVLSI 2022 paper: iMAD: An In-Memory Accelerator for AdderNet with Efficient 8-bit Addition and Subtraction Operations
Replication Data for: TAB: Unified and Optimized Ternary, Binary and Mixed-Precision Neural Network Inference on the Edge
May 10, 2022
Zhu, Shien; Duong, H. K. Luan; Liu, Weichen, 2022, "Replication Data for: TAB: Unified and Optimized Ternary, Binary and Mixed-Precision Neural Network Inference on the Edge", https://doi.org/10.21979/N9/RZ75BY, DR-NTU (Data), V1, UNF:6:Md7is6scgmHZ4O3PtUuAOg== [fileUNF]
The TAB experiment code running on ARM CPU, x86 CPU, and Nvidia GPU.
Replication Data for: XOR-Net: An Efficient Computation Pipeline for Binary Neural Network Inference on Edge Devices
Dec 21, 2020
Zhu, Shien; Duong, H. K. Luan; Liu, Weichen, 2020, "Replication Data for: XOR-Net: An Efficient Computation Pipeline for Binary Neural Network Inference on Edge Devices", https://doi.org/10.21979/N9/XEH3D1, DR-NTU (Data), V1, UNF:6:5DOBB66c624HMnkRD7Qw9g== [fileUNF]
Accepted as a conference paper by IEEE International Conference on Parallel and Distributed Systems (ICPADS) 2020.
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