Weight quantization in Boltzmann machines

By W. Balzer, Masanobu Takahashi, J. Ohta, K. Kyuma. 1991

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Secure Evaluation of Quantized Neural Networks (2019, background): “representation of the weights and activations to be as low as 8 bits, or even 1 bit in some cases [11, 53].3 This is a long-standing research area, with initial works already dating back to the 1990s [23, 4, 61, 48], and this extensive research body have enabled modern quantized neural networks to h”
A Survey on Methods and Theories of Quantized Neural Networks (2018, background): “Quantizing neural networks dates back to the 1990s [ Fiesler et al. , 1990; Balzer et al. , 1991; Tang and Kwan, 1993; Marchesi et al. , 1993 ] .”
Pruning and Quantization for Deep Neural Network Acceleration: A Survey (2021, background): “Converting floating-point parameters into low numerical precision datatypes for quantizing neural network was proposed as far back as the 1990s [63, 14].”
Training Spiking Neural Networks Using Lessons From Deep Learning (2021, background): “Training quantized neural networks involves adjusting the weights and activations to use lower-precision fixed-point representations while maintaining acceptable performance and accuracy [179, 180].”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “Quantization has long been employed to reduce the network size for efficient storage [133], [134].”