Efficient VHDL Simulation of Convolutional Neural Network Using Parallel Pipelining
Abstract
Full Text:
PDFReferences
Daewoo Kim, Mansureh S. Moghaddam Hossein Moradian, Hyeonuk Sim, Jongeun Lee, Kiyoung Choi, “FPGA Implementation of Convolutional Neural Network Based on Stochastic Computing”, 978-1-5386-2656-6/17/$31.00©2017 IEEE.
Mohammed Alaward, and Mingjie Lin, “Stochastic-Based Multi-Stage Streaming Realization of Deep Convolutional Neural Network”,978-1-5090-5404-6/17/$31.00©2017 IEEE.
Pooja Samudre, Prashant Shende, Vishal Jaiswal, “Optimizing Performance of Convolutional Neural Network Using Computing Teqchnique”. In IEEE 5th International conference for convergence in technology,Pune, March 2019.
Babak Zamanlooy, and Mitra Mirhassani, “Efficient VLSI Implementation of Neural Networks With Hyperbolic Tangent Activation Fuction”, 1063-8210©2013 IEEE.
A. Krizhevsky et al., “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25, F. Pereira et al., Eds. Curran Associates, Inc., 2012, pp. 1097–1105.
F. N. Iandola et al., “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size,” CoRR, vol. abs/1602.07360, 2016.
Chen, Yu-Hsin and Krishna, Tushar and Emer, Joel and Sze, Vivienne, “Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks,” in ISSCC 2016, 2016, pp. 262–263.
N. P. Jouppi et al., “In-Datacenter Performance Analysis of a Tensor Processing Unit,” ArXiv e-prints, Apr. 2017.
C. Zhang et al., “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in FPGA ’15. New York, NY, USA: ACM, 2015, pp. 161–170.
S. Han et al., “Eie: Efficient inference engine on compressed deep neural network,” in ISCA ’16. Piscataway, NJ, USA: IEEE Press, 2016, pp. 243–254.
J. Albericio et al., “Cnvlutin: Ineffectual-neuron-free deep neural network computing,” in Proceedings of the 43rd International Symposium on Computer Architecture, ser. ISCA ’16. Piscataway, NJ, USA: IEEE Press, 2016.
H. Sim and J. Lee, “A new stochastic computing multiplier with application to deep convolutional neural networks,” in 54th Annual ACM/IEEE Design Automation Conference (DAC ’17), Jun. 2017, pp. 29:1–29:6.
LeCun, Y. and Y. Bengio, 1995. “Convolutional Networks for Images, Speech, and Time Series”. The Handbook of Brain Theory and Neural Networks, 3361(10): 1-14.
LeCun, Y., F.J. Huang and L. Bottou, 2004. “Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting”. In the Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp: 1-8.
Introduction to convolution neural network (online) https://en.wikipedia.org/wiki/Yann_LeCun.
Back-propagation learning in CNN and VHDL Implementation (online)
https://www.ijser.org
Introduction to Quartus II version 10.1 SPI software tool
https://www.intel.cn/content/dam/alterawww/global/en.../pdfs/.../rn_qts_101sp1.pdf
Refbacks
- There are currently no refbacks.