Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA

Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

Original

Han, S., Mao, H., and Dally, W. J · 2015

Earlier work this paper cites.

Tensorflow: A system for large-scale machine learning

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016

Earlier work this paper cites.

Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1

Original

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

Earlier work this paper cites.

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

Original

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

Cited alongside, same era.

Ternary weight networks

Original

Li, F., Zhang, B., and Liu, B · 2016

Cited alongside, same era.

Yolo9000: better, faster, stronger

Original

Redmon, J., and Farhadi, A · 2016

Cited alongside, same era.

Mobilenets: Efficient convolutional neural networks for mobile vision applications

Original

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017

Closest in time.

Finn: A framework for fast, scalable binarized neural network inference

Original

Umuroglu, Y., Fraser, N. J., Gambardella, G., Blott, M., Leong, P., Jahre, M., and Vissers, K · 2017

Closest in time.