Quantized low-precision neural networks are very popular because they require less
computational resources for inference and can provide high performance, which is vital for …

One of the most computationally intensive parts in modern recognition systems is an
inference of deep neural networks that are used for image classification, segmentation …

The priority of building hardware-oriented neural network models is growing steadily. The
target goals for their development are the performance and energy efficiency of promising …

Quantized neural networks (QNNs) are widely used to achieve computationally efficient
solutions to recognition problems. Overall, eight-bit QNNs have almost the same accuracy …

In the paper we introduce a novel bipolar morphological neuron and bipolar morphological
layer models. The models use only such operations as addition, subtraction and maximum …

Abstract Machine learning algorithms for medical diagnostics often require resource-
intensive environments to run, such as expensive cloud servers or high-end GPUs, making …

In this work we study the effect of activation functions in a neural network. We consider how
activation functions with different properties and their combination affect the final quality of …

Deep neural networks are widely used in various AI systems. Many such systems rely on the
edge computing concept and try to perform computations on end devices while still being …

This paper discusses a task of document recognition on a sequence of video frames. In
order to optimize the processing speed an estimation is performed of stability of recognition …

The implementations of the convolution operation in neural networks are usually based on
convolution-to-GeMM (General Matrix Multiplication) transformation. However, this …