XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks

Built on

A. Canziani, A. Paszke, and E. Culurciello, “An analysis of deep neural network models for practical applications,” CoRR , vol. abs/1605.07678, 2016
Original
2016
Earlier work this paper cites.
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnor-net: Imagenet classification using binary convolutional neural networks,” in European Conference on Computer Vision . Springer, 2016, pp. 525–542
2016
Earlier work this paper cites.

X. Lin, C. Zhao, and W. Pan, “Towards accurate binary convolutional neural network,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 345–353
2017
Cited alongside, same era.
Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. Leong, M. Jahre, and K. Vissers, “Finn: A framework for fast, scalable binarized neural network inference,” in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays . ACM, 2017, pp. 65–74
2017
Cited alongside, same era.

R. Andri, L. Cavigelli, D. Rossi, and L. Benini, “YodaNN: An architecture for ultralow power binary-weight cnn acceleration,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , vol. 37, no. 1, pp. 48–60, 2018
2018
Closest in time.