In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …

Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network
(DNN) training by providing a wide dynamic range via a shared exponent across a group of …

Deep neural networks (DNNs) have shown great success in completing complex tasks.
However, DNNs inevitably bring high computational cost and storage consumption due to …

Deep learning techniques and tools have experienced enormous growth and widespread
diffusion in recent years. Among the areas where deep learning has become more …

The development of power-efficient Machine Learning Hardware is of high importance to
provide Artificial Intelligence (AI) characteristics to those devices operating at the Edge …

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

This paper presents a novel approach for accurate barcodes detection in real and
challenging environments using compact deep neural networks. Our approach is based on …

Modern visual pattern recognition methods are based on convolutional networks since they
are able to learn complex patterns directly from the data. However, convolutional networks …

Emerging edge devices such as sensor nodes are increasingly being tasked with non-trivial
tasks related to sensor data processing and even application-level inferences from this …