Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start

R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning

1992

Earlier work this paper cites.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition

2009

Earlier work this paper cites.

S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” arXiv preprint arXiv:1510.00149

Original

2015

Earlier work this paper cites.

C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in Proceedings of the 2015 ACM/SIGDA international symposium on field-programmable gate arrays

2015

Earlier work this paper cites.

W. Jiang, E. H.-M. Sha, Q. Zhuge, and X. Chen, “Optimal functional-unit assignment and buffer placement for probabilistic pipelines,” in Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

2016

Earlier work this paper cites.

W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” in Advances in neural information processing systems

2016

Earlier work this paper cites.

C. Zhang, D. Wu, J. Sun, G. Sun, G. Luo, and J. Cong, “Energy-efficient cnn implementation on a deeply pipelined fpga cluster,” in Proceedings of the 2016 International Symposium on Low Power Electronics and Design

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2016

Earlier work this paper cites.

H. Mao, S. Han, J. Pool, W. Li, X. Liu, Y. Wang, and W. J. Dally, “Exploring the regularity of sparse structure in convolutional neural networks,” arXiv preprint arXiv:1705.08922

Original

2017

Earlier work this paper cites.

E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. Le, and A. Kurakin, “Large-scale evolution of image classifiers,” arXiv preprint arXiv:1703.01041

Original

2017

Earlier work this paper cites.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2017

Earlier work this paper cites.

H. Cai, L. Zhu, and S. Han, “Proxylessnas: Direct neural architecture search on target task and hardware,” arXiv preprint arXiv:1812.00332

Original

2018

Earlier work this paper cites.

H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” arXiv preprint arXiv:1806.09055

Original

2018

Cited alongside, same era.

W. Jiang, E. H.-M. Sha, Q. Zhuge, L. Yang, X. Chen, and J. Hu, “Heterogeneous fpga-based cost-optimal design for timing-constrained cnns,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

2018

Cited alongside, same era.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition

Original

2018

Cited alongside, same era.

B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer, “Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

2019

Cited alongside, same era.

W. Jiang, E. H.-M. Sha, X. Zhang, L. Yang, Q. Zhuge, Y. Shi, and J. Hu, “Achieving super-linear speedup across multi-fpga for real-time dnn inference,” ACM Transactions on Embedded Computing Systems (TECS)

2019

Later among the works it cites.

K. Wang, Z. Liu, Y. Lin, J. Lin, and S. Han, “Haq: Hardware-aware automated quantization with mixed precision,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2019

Later among the works it cites.

W. Jiang, L. Yang, E. H.-M. Sha, Q. Zhuge, S. Gu, S. Dasgupta, Y. Shi, and J. Hu, “Hardware/software co-exploration of neural architectures,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

2020

Closest in time.

W. Jiang, Q. Lou, Z. Yan, L. Yang, J. Hu, X. S. Hu, and Y. Shi, “Device-circuit-architecture co-exploration for computing-in-memory neural accelerators,” IEEE Transactions on Computers

2020

Closest in time.