Deep learning models are being deployed in many mobile intelligent applications. End-side
services, such as intelligent personal assistants, autonomous cars, and smart home services …

This paper proposes ReBNet, an end-to-end framework for training reconfigurable binary
neural networks on software and developing efficient accelerators for execution on FPGA …

In today's computing technology scene, mobile devices are considered to be
computationally weak, while large cloud servers are capable of handling expensive …

In this article, a methodology based on machine learning for fault detection in continuous
processes is presented. It aims to monitor fully distributed scenarios, such as the Tennessee …

We live in a world where technological advances are continually creating more data than
what we can deal with. Machine learning algorithms, in particular Deep Neural Networks …

This paper proposes CodeX, an end-to-end framework that facilitates encoding, bitwidth
customization, fine-tuning, and implementation of neural networks on FPGA platforms …

Convolutional neural networks (CNNs) have become a primary approach in the field of
artificial intelligence (AI), with wide range of applications. The two computational phases for …

Thanks to the machine learning algorithms revolution, several organizations will be able to
transform their services, automate functions and predict their customer's behaviors. The fact …

Major advancements in building general-purpose and customized hardware have been one
of the key enablers of versatility and pervasiveness of machine learning models such as …

Many applications, such as machine learning and data sensing, are statistical in nature and
can tolerate some level of inaccuracy in their computation. A variety of designs have been …