Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)

And the bit goes down: Revisiting the quantization of neural networks

Original

Stock, P., Joulin, A., Gribonval, R., Graham, B., and Jégou, H · 1907

Earlier work this paper cites.

Estimating or propagating gradients through stochastic neurons for conditional computation

Original

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

Earlier work this paper cites.

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

Horowitz, M · 2014

Earlier work this paper cites.

Batch normalization: Accelerating deep network training by reducing internal covariate shift

Ioffe, S. and Szegedy, C · 2015

Earlier work this paper cites.

Quantization and training of neural networks for efficient integer-arithmetic-only inference

Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D · 2018

Earlier work this paper cites.

Quantizing deep convolutional networks for efficient inference: A whitepaper

Original

Krishnamoorthi, R · 2018

Cited alongside, same era.

HAWQ: hessian aware quantization of neural networks with mixed-precision

Dong, Z., Yao, Z., Gholami, A., Mahoney, M. W., and Keutzer, K · 2019

Cited alongside, same era.

Data-free quantization through weight equalization and bias correction

Nagel, M., van Baalen, M., Blankevoort, T., and Welling, M · 2019

Cited alongside, same era.

Dsconv: Efficient convolution operator

Nascimento, M. G. d., Fawcett, R., and Prisacariu, V. A · 2019

Cited alongside, same era.

Learned step size quantization

Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., and Modha, D. S · 2020

Cited alongside, same era.

Up or down? Adaptive rounding for post-training quantization

Nagel, M., Amjad, R. A., Van Baalen, M., Louizos, C., and Blankevoort, T · 2020

Later among the works it cites.

Pushing the limits of narrow precision inferencing at cloud scale with microsoft floating point

Rouhani, B., Lo, D., Zhao, R., Liu, M., Fowers, J., Ovtcharov, K., Vinogradsky, A., Massengill, S., Yang, L., Bittner, R., Forin, A., Zhu, H., Na, T., Patel, P., Che, S., Koppaka, L. C., Song, X., Som, S., Das, K., Tiwary, S., Reinhardt, S., Lanka, S., Chung, E., and Burger, D · 2020

Later among the works it cites.

Mixed precision dnns: All you need is a good parametrization

Uhlich, S., Mauch, L., Cardinaux, F., Yoshiyama, K., Garcia, J. A., Tiedemann, S., Kemp, T., and Nakamura, A · 2020

Later among the works it cites.

Bayesian bits: Unifying quantization and pruning

van Baalen, M., Louizos, C., Nag el, M., Amjad, R. A., Wang, Y., Blankevoort, T., and Welling, M · 2020

Later among the works it cites.

A white paper on neural network quantization, 2021

Nagel, M., Fournarakis, M., Amjad, R. A., Bondarenko, Y., van Baalen, M., and Blankevoort, T · 2021

Later among the works it cites.