Category Archive for : ‘FPGA’

Abstract

This article explains how to engineer an IoT on Edge solution, a multisensor scenario with a wide range of communication protocols. The solution proposed is about an implementation of the complete stack to satisfy hardware and software requirements needed for IoT with massive data exchange. The engineering samples proposed is a unique System On Module (SOM) or MultiProcessor System On Chip (MPSoC) solution based on Xilinx Zynq family which allows to manage a high data throughput and low latency when it comes to data processing.

Statement of the problem

The evolution of

Introduction

In this article I am going to explain the concept of predictive maintenance, and why it should be used in any actual application which suffers from time deterioration.

The example application we will focus on is the status report of an electric drive, used in modern applications such as autonomous vehicles or industrial robotics.

The aim of the predictive maintenance is to predict ahead of time eventual faults of the system based on the current status.

We now briefly introduce electrical drives and motor control and then use them to understand how to

Introduction

This article is aimed to present in a comprehensible way the new Versal ACAP device developed by Xilinx Inc.

Let’s start from the signature, what does ACAP mean? ACAP stands for Adaptive Computing Acceleration Platform, which enlightens the actual goal achieved by Xilinx with this new generation of platform, or rather to create a fully software programmable platform.

The new Versal generation, in fact, with the support of the new Vitis IDE, allows every type of programmer, from Data Scientists to Hardware Engineers, to deploy their applications.

The new Versal Architecture is based

FPGA Based EdgeAI Gateway with support of AI Frameworks enabling AI Solutions iWave Systems and MakarenaLabs partner to provide comprehensive EdgeAI Platforms 

Through a collaboration with MakarenaLabs srl, a leader in scientific research in multimedia recognition and feature extractions, iWave Systems is excited to collaborate with MakarenaLabs enabling customers with rugged AI Solutions and edge computing systems. Combining the strong edge computing and FPGA Machine Learning expertise of iWave Systems & MakarenaLabs, through this partnership we aim to deliver an optimized AI platform deploying AI to the edge and helping customers

1 –
Introduction

Generally we are used to see articles or demos on
deep/machine learning methods just on video applications. In this article we
want review and apply the current AI into future trends for robotics.

Let say that there is not a general definition for AI,
but the one we selected is that a system is “intelligent” if it achieves a
specific goal in a generic situation.

We can define a robotic system as a complex system composed of many heterogeneous components, generally we would have cameras for the perception part, the mechanics which physically

IoT is an acronym for the Internet of Things, it represents a new working model based on connected devices which is used in modern companies to create new revolutionary applications.

The term [1] was coined to Kevin Ashton nowadays co-founder of Auto-ID Labs who used it for the first time in 1999 in a proposal of a pioneering project to monitor the performance and yields of the assembly lines of Procter & Gamble (P&G) through the sharing of RFID devices on the network.

Figura 1. IoT keywords

An indicator of being

Pynq is a famous framework that allows programmers to directly work with FPGA using Python language, which is very intuitive, so let’s take a look at it.

When we want to accelerate a specific software portion, it is generally difficult to interface to it correctly, because you have to synchronize the CPU (generally referred to as Process System, PS) to the FPGA part (generally referred to as Programmable Logic, PL). In modern SoCs, we use the “axi” interface to make the two parts communicate, but its Hardware Description Language (HDL) implementation

Artificial Neural Networks

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules; so you can get it to learn things, recognize patterns, and make decisions in a “human-like” way. The patterns recognized are numerical, contained in tensors, into which all real-world data, be it images, sound, text or time series, must be translated.

Difference between traditional programming and machine learning (Laurence Moroney and Karmel Allison)

How does a neural
network learn?

Information flows

https://www.linkedin.com/pulse/digital-twin-xilinx-alveo-vitis-sps-trade-show-giulio-corradi-1f/

After 30 years of SPS IPC Drives, the trade show changed the name into SPS – smart production solutions reflecting the digital transformation that occurs in automation technology.

Digital Twin (DT) in relation to Cyber-Physical-Systems (CPS) are among the main themes of this digital transformation anticipated by the Industry 4.0 initiative.

According to DirectIndustry e-magazine interview to Sylke Schulz-Metzner, Vice President SPS Mesago Messe Frankfurt GmbH, “digital twins is sure to play a large role this year and in the future at SPS…In the future, it will be as difficult to imagine the world