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20192023
most citedOn Neural Architecture Search for Resource-Constrained Hardware Platforms

59 citations · 267 across the 31 of their papers we have counts for

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Showing 2021Show all

9 papers · 1 filter

cs.LG2021★ 27 cited

One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search

Bingqian Lu, Jianyi Yang, Weiwen Jiang +2

Convolutional neural networks (CNNs) are used in numerous real-world applications such as vision-based autonomous driving and video content analysis. To run CNN inference on variou…

cs.LG2021

RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions

Sung-En Chang, Yanyu Li, Mengshu Sun +4

This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the firs…

quant-ph2021★ 7 cited

Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow

Zhiding Liang, Zhepeng Wang, Junhuan Yang +4

In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error corr…

cs.CL2021★ 11 cited

Detecting Gender Bias in Transformer-based Models: A Case Study on BERT

Bingbing Li, Hongwu Peng, Rajat Sainju +7

In this paper, we propose a novel gender bias detection method by utilizing attention map for transformer-based models. We 1) give an intuitive gender bias judgement method by comp…

cs.AR2021★ 1 cited

Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search

Hongwu Peng, Shiyang Chen, Zhepeng Wang +9

Molecular similarity search has been widely used in drug discovery to identify structurally similar compounds from large molecular databases rapidly. With the increasing size of ch…

cs.LG2021★ 1 cited

RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search

Zheyu Yan, Weiwen Jiang, Xiaobo Sharon Hu +1

Differentiable neural architecture search (DNAS) is known for its capacity in the automatic generation of superior neural networks. However, DNAS based methods suffer from memory u…