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11/15/2022
HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers
While vision transformers (ViTs) have continuously achieved new mileston...
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10/30/2021
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
This work targets the commonly used FPGA (field-programmable gate array)...
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10/30/2021
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
This work proposes a novel Deep Neural Network (DNN) quantization framew...
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12/08/2020
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Deep Neural Networks (DNNs) have achieved extraordinary performance in v...
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09/16/2020
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework
With the tremendous success of deep learning, there exists imminent need...
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07/20/2020
Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
Mobile devices are becoming an important carrier for deep learning tasks...
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02/25/2020
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
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01/23/2020
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency
Structured weight pruning is a representative model compression techniqu...
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10/18/2019
Evading Real-Time Person Detectors by Adversarial T-shirt
It is known that deep neural networks (DNNs) could be vulnerable to adve...
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04/03/2019
