Short floating-point representation for convolutional neural network inference

[1]  Sherief Reda,et al.  Understanding the impact of precision quantization on the accuracy and energy of neural networks , 2016, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[2]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[3]  Song Han,et al.  DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow , 2016, ArXiv.

[4]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Shaoli Liu,et al.  Cambricon: An Instruction Set Architecture for Neural Networks , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

[6]  Bin Liu,et al.  Ternary Weight Networks , 2016, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[9]  Soheil Ghiasi,et al.  Hardware-oriented Approximation of Convolutional Neural Networks , 2016, ArXiv.

[10]  Vikas Chandra,et al.  Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations , 2017, ArXiv.

[11]  Yoshua Bengio,et al.  Training deep neural networks with low precision multiplications , 2014 .

[12]  Natalie D. Enright Jerger,et al.  Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets , 2015, ArXiv.

[13]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ninghui Sun,et al.  DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning , 2014, ASPLOS.

[16]  Joel Emer,et al.  Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks , 2016, CARN.

[17]  Pritish Narayanan,et al.  Deep Learning with Limited Numerical Precision , 2015, ICML.

[18]  Sachin S. Talathi,et al.  Fixed Point Quantization of Deep Convolutional Networks , 2015, ICML.

[19]  Wonyong Sung,et al.  Resiliency of Deep Neural Networks under Quantization , 2015, ArXiv.

[20]  Daisuke Miyashita,et al.  Convolutional Neural Networks using Logarithmic Data Representation , 2016, ArXiv.

[21]  Ali Farhadi,et al.  XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.

[22]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[23]  Reza Ebrahimpour,et al.  A Resource-Limited Hardware Accelerator for Convolutional Neural Networks in Embedded Vision Applications , 2017, IEEE Transactions on Circuits and Systems II: Express Briefs.