This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

Binary neural networks (BNNs) are variations of artificial/deep neural network (ANN/DNN)
architectures that constrain the real values of weights to the binary set of numbers {− 1, 1} …

In this work, a new custom design of an anomaly detection and classification system is
proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform …

Abstract Machine learning as a service (MLaaS) has risen to become a prominent
technology due to the large development time, amount of data, hardware costs, and level of …

This paper proposes a novel low-power HW accelerator for audio PDM-to-PCM conversion
based on artificial neural network. The system processes samples from a digital MEMS …

Binary neural networks (BNNs) have attracted significant interest for the implementation of
deep neural networks (DNNs) on resource-constrained edge devices, and various BNN …

Abstract Wi-Fi Channel State Information (CSI) based human activity recognition (HAR)
which using channel disturbances caused by signal reflection is a novel way of environment …

The accuracy and computational complexity of keyword spotting (KWS) systems are heavily
influenced by the choice of audio features in speech signals. This letter introduces a novel …

Fine-tuning deep neural networks is pivotal for creating inference modules that can be
suitably imported to edge or field-programmable gate array (FPGA) platforms. Traditionally …

This brief proposes a new approach based on a tiny neural network to convert Pulse Density
Modulation (PDM) signals acquired from digital Micro-Electro-Mechanical System (MEMS) …