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Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present, latency as well.
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B. Zoph and Q. V. Le, “Neural Architecture Search with Reinforcement Learning,” arXiv e-prints , Nov 2016
2016
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M. Tan, B. Chen, R. Pang et al. , “MnasNet: Platform-Aware Neural Architecture Search for Mobile,” arXiv e-prints , Jul 2018
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2019
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2019
Cited alongside, same era.
J. Fowers, K. Ovtcharov, M. Papamichael et al. , “A Configurable Cloud-scale DNN Processor for Real-time AI,” in International Symposium on Computer Architecture (ISCA)
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M. S. Abdelfattah, D. Han, A. Bitar et al. , “DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration,” in International Conference on Field Programmable Logic and Applications (FPL)
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N. P. Jouppi, C. Young, N. Patil et al. , “In-datacenter performance analysis of a tensor processing unit,” in International Symposium on Computer Architecture (ISCA)
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C. Hao, X. Zhang, Y. Li et al. , “Fpga/dnn co-design: An efficient design methodology for iot intelligence on the edge,” in Design Automation Conference (DAC)
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D. R. So, C. Liang, and Q. V. Le, “The Evolved Transformer,” arXiv e-prints , Jan 2019
2019
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W. Jiang, L. Yang, E. Sha et al. , “Hardware/Software Co-Exploration of Neural Architectures,” arXiv e-prints , Jul 2019
2019
Later among the works it cites.
C. Ying, A. Klein, E. Real et al. , “NAS-Bench-101: Towards Reproducible Neural Architecture Search,” arXiv e-prints , Feb 2019
2019
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X. Inc., “Chaidnnv2 - hls based dnn accelerator library for xilinx ultrascale+ mpsocs,” https://github.com/Xilinx/CHaiDNN, 2019
2019
Later among the works it cites.
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