DeepBurning: Automatic generation of FPGA-based learning accelerators for the Neural Network family

Proceedings - Design Automation Conference

Volumn 2016-August, Issue , 2016, Pages

  Wang, Ying ^a   Xu, Jie ^a   Han, Yinhe ^a   Li, Huawei ^a   Li, Xiaowei ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Hardware Software co simulation; High Level Synthesis; SoC design; Verification

Indexed keywords

ACCELERATION; ARTIFICIAL INTELLIGENCE; AUTOMATION; COMPUTER AIDED DESIGN; COMPUTER VISION; DESIGN; ENERGY EFFICIENCY; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); HARDWARE; HIGH LEVEL SYNTHESIS; LEARNING SYSTEMS; PROGRAMMABLE LOGIC CONTROLLERS; RECONFIGURABLE HARDWARE; SYSTEM-ON-CHIP; VERIFICATION;

APPLICATION DEVELOPERS; AUTOMATIC GENERATION; COSIMULATION; DESIGN-AUTOMATION TOOLS; NEURAL NETWORK FAMILY; OPTIMIZED PERFORMANCE; SOC DESIGNS; USER-SPECIFIED CONSTRAINTS;

INTEGRATED CIRCUIT DESIGN;

EID: 84985993737     PISSN: 0738100X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2897937.2898002     Document Type: Conference Paper

References (11)

1. Esmaeilzadeh et al., "Neural acceleration for general-purposeapproximate programs,". In Proc. MICRO, 2012.
2. G. Hinton et al., "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups," in IEEE Signal Processing Magazine,, vol. 29, no. 6, pp. 82-97, 2012.
3. Y Jia et al., "Caffé: Convolutional architecture for fast feature embedding," in ACM Proc. Multimedia, 2014.
4. T. Chen et al., "DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning,"in Proc. ASPLOS, 2014.
5. A. Krizhevsky et al., "Imagenet classification with deep convolutional neural networks," in Proc. NIPS, 2012.
6. M. Peemen et al., "Memory-centric accelerator design for convolutional neural networks," in Proc. ICCD, 2013.
7. C. Zhang et al., "Optimizing FPGA-based accelerator design for deep convolutional neural networks,"in Proc. FPGA, 2015.
8. W. Ouyang et al,. "DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection," in Proc. CVPR, 2015.
9. Y. Pan et. al., "Jointly Modeling Embedding and Translation to Bridge Video and Language," in arXiv, 2015
10. V. Vapnik, "Does Deep Learning Come from the Devil?," Yandex conference on machine learning prospects and applications, Berlin, 2015
11. R. Beigel et al., "Sorting n Objects With a k-Sorter,"IEEE Trans, on Compute., 1990.