DNN Feature Map Compression using Learned Representation over GF(2)

Reducing the dimensionality of data with neural networks

Hinton, G., Salakhutdinov, R.: · 2006

Earlier work this paper cites.

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2010

Earlier work this paper cites.

Estimating or propagating gradients through stochastic neurons for conditional computation

Original

Bengio, Y., Léonard, N., Courville, A.: · 2013

Earlier work this paper cites.

Caffe: Convolutional architecture for fast feature embedding

Original

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014

Earlier work this paper cites.

Computing’s energy problem (and what we can do about it)

Horowitz, M.: · 2014

Earlier work this paper cites.

Deep residual learning for image recognition

Original

He, K., Zhang, X., Ren, S., Sun, J.: · 2015

Earlier work this paper cites.

Training deep neural networks with low precision multiplications

Courbariaux, M., Bengio, Y., David, J.: · 2015

Earlier work this paper cites.

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015

Earlier work this paper cites.

Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5MB model size

Original

Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: · 2016

Cited alongside, same era.

SSD: Single shot multibox detector

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016

Cited alongside, same era.

Hardware-oriented approximation of convolutional neural networks

Gysel, P., Motamedi, M., Ghiasi, S.: · 2016

Cited alongside, same era.

Binarized neural networks

Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: · 2016

Cited alongside, same era.

XNOR-net: Imagenet classification using binary convolutional neural networks

Original

Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: · 2016

Cited alongside, same era.

Fused-layer CNN accelerators

Alwani, M., Chen, H., Ferdman, M., Milder, P.A.: · 2016

Later among the works it cites.

Speed/accuracy trade-offs for modern convolutional object detectors

Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., Murphy, K.: · 2017

Later among the works it cites.

Submanifold sparse convolutional networks

Original

Graham, B., van der Maaten, L.: · 2017

Later among the works it cites.

Sigma delta quantized networks

O’Connor, P., Welling, M.: · 2017

Later among the works it cites.

How to train a compact binary neural network with high accuracy?

Tang, W., Hua, G., Wang, L.: · 2017

Later among the works it cites.

SCNN: An accelerator for compressed-sparse convolutional neural networks

Parashar, A., Rhu, M., Mukkara, A., Puglielli, A., Venkatesan, R., Khailany, B., Emer, J., Keckler, S.W., Dally, W.J.: · 2017