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 …

Convolutional neural networks require significant memory bandwidth and storage for
intermediate computations, apart from substantial computing resources. Neural network …

Benefiting from tens of millions of hierarchically stacked learnable parameters, Deep Neural
Networks (DNNs) have demonstrated overwhelming accuracy on a variety of artificial …

We propose a novel value-aware quantization which applies aggressively reduced
precision to the majority of data while separately handling a small amount of large data in …

Quantization, which involves bit-width reduction, is considered as one of the most effective
approaches to rapidly and energy-efficiently deploy deep convolutional neural networks …

Convolutional Neural Networks (CNNs) have proven to be a powerful state-of-the-art
method for image classification tasks. One drawback however is the high computational …

Quantization for deep neural networks have afforded models for edge devices that use less
on-board memory and enable efficient low-power inference. In this paper, we present 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 …

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 …

Quantizing weights and activations of deep neural networks results in significant
improvement in inference efficiency at the cost of lower accuracy. A source of the accuracy …