Deep neural networks with adaptive configurations have gained increasing attention due to
the instant and flexible deployment of these models on platforms with different resource …

Abstract Model quantization has emerged as a mandatory technique for efficient inference
with advanced Deep Neural Networks (DNN). It converts the model parameters in full …

Abstract Model quantization is an important mechanism for energy-efficient deployment of
deep neural networks on resource-constrained devices by reducing the bit precision of …

Abstract Model size and inference speed/power have become a major challenge in the
deployment of neural networks for many applications. A promising approach to address …

Recent machine learning methods use increasingly large deep neural networks to achieve
state of the art results in various tasks. The gains in performance come at the cost of a …

Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently
employed in popular efficient architectures can also result in negative activation values, with …

When training neural networks with simulated quantization, we observe that quantized
weights can, rather unexpectedly, oscillate between two grid-points. The importance of this …

We introduce a data-free quantization method for deep neural networks that does not
require fine-tuning or hyperparameter selection. It achieves near-original model …

Quantization is a popular way of increasing the speed and lowering the memory usage of
Convolution Neural Networks (CNNs). When labelled training data is available, network …

Deep learning algorithms achieve high classification accuracy at the expense of significant
computation cost. To address this cost, a number of quantization schemes have been …