Convolutional neural networks are a promising tool for solving the problem of pattern
recognition. Most well-known convolutional neural networks implementations require a …

Residue number systems (RNSs) and arithmetic are useful for several reasons. First, a great
deal of computing now takes place in embedded processors, such as those found in mobile …

We show that garbled circuits are a practical choice for secure evaluation of neural network
classifiers. At the protocol level, we start with the garbling scheme of Ball, Malkin & Rosulek …

The residue number system (RNS) is an unconventional number system which can lead to
parallel and fault-tolerant arithmetic operations. However, the complexity of residue-to …

Convolutional Neural Network plays a vital role in Artificial intelligence and it's mainly
harnessed for pattern recognition. However, it's quite tricky to achieve less image …

The use of parallel computing in the field of digital signal processing (DSP) is associated
with the continuous growth of performance requirements of computing facilities [1]-[3]. But at …

The residue number system (RNS) provides parallel, carry-free, and high-speed arithmetic
and is therefore a good tool for high-performance computing. However, operations such as …

Sign determination is a fundamental problem in algebraic as well as geometric computing. It
is the critical operation when using real algebraic numbers and exact geometric predicates …

This paper considers the method of pattern recognition based on a convolutional neural
network using Sobel filters. Parameters of the convolutional neural network blocks were …

The adoption of machine learning solutions is rapidly increasing across all parts of society.
As the models grow larger, both training and inference of machine learning models is …