Category Archive for : ‘Machine Learning’

Introduction

The integration of machine vision capabilities with the KR260 board and the Sony IMX547 Camera presents a powerful solution for image processing applications. This tutorial revisits the deployment process outlined in the official documentation here, providing step-by-step instructions to accelerate your setup.

Installation of Ubuntu on KR260

Begin by installing the official Ubuntu image for KR260 on a new SD card. You will find the image here.

Download the Balena Etcher (recommended; available for Windows, Linux, and macOS). Follow the instructions in the tool and select the downloaded image to flash onto your

MuseBox Webinar

Join us on September 28th at 2 PM CEST for an exciting Avnet Silica webinar, presented with MakarenaLabs! We’re thrilled to take you on a deep-dive journey into the world of MuseBox. Here,  we’ll explore its diverse use cases, enablement options, and outstanding support system.

During this webinar, you’ll have the chance to uncover the remarkable capabilities of MuseBox. Also, you get a comprehensive understanding of the supported platforms, with a special focus on KRIA. Through engaging live demonstrations, we’ll vividly showcase the sheer power of MuseBox in action –

Introduction

Collaborative robotics and facial emotion classification are two related fields that can benefit from each other. Collaborative robotics aims to create robots that can work safely and efficiently with humans in a shared environment. Facial emotion classification aims to analyze human facial expressions and recognize the underlying emotions. By combining these two fields, we can create robots that can not only perform tasks with humans but also understand and respond to their emotions. This can enhance the naturalness and empathy of the human-robot interaction, and improve the performance and satisfaction

Announcement
The new release of MuseBox (2.0, aka “CLIO”) has a huge amount of machine learning tasks (both audio and video) with the new implementation of a no-code graphic interface. In particular, this graphic interface allows you to compose your machine learning pipeline without any expertise in Vitis, Vivado, and software in general. Just open and use your browser and your imagination!

The new release allows you to execute live the entire machine learning pipeline without any interaction with the backend software. In particular, you don’t need to restart any process on

Main Characteristics

This is the second article of a series, which aim is a feasibility study for a modular system composed of the main control board based on Xilinx ZYNQ Ultrascale+ and servo satellite motor drives based on Xilinx ZYNQ-7000. The objective of the system is to control a robot composed of multiple joints. The article’s target is an automation of a prostate biopsy to assist doctors during medical procedures. The biopsy is executed by driving one mechanical positioner composed of 6 Permanent Magnet Synchronous Motors (PMSM) motors and multiple joints

Introduction

This article will talk about the Ultrasound Ultrafast Imaging modality on the new AMD-Xilinx Versal chip. The work described here is an implementation by MakarenaLabs for AMD-Xilinx and is currently in early access for customers.

To begin with, we will briefly introduce the UltraFast methodology and what does it mean.

Secondly we are going to talk about Versal and how to create and partition design on this new technology.

Lastly we are going to talk about the Ultrasound Libraries on the new Versal chip and how to exploit them.

UltraFast Imaging

UltraFast Imaging is a

MuseBox is the MakarenaLabs flagship product and we make it available to you a new on-premise Demo on the MuseBox site. We show seamless integration of AI applications simultaneously on several different Xilinx AMD hardware. From Ultra96 and Kria, U200 and U50 Alveo cards up to VCK5000. You can execute AI inference from your web browser wherever you are, on any of the AI models we made available, running them in real-time on the boards and getting performance results.

What is MuseBox?

MuseBox ( https://musebox.it ) is a production system for extracting

Introduction

In this tutorial, we will show how to create a custom hardware design with Xilinx’s DPU IP provided in the Vitis AI toolchain. Specifically, you can use this approach with any custom design which is Vivado compatible, and you can extend it to infer AI models.

What is the DPU IP

The Xilinx® Deep Learning Processor Unit (DPU) is a programmable engine dedicated for convolutional neural network. The unit contains register configure module, data controller module, and convolution computing module. There is a specialized instruction set for DPU, which enables DPU to

Introduction​

The topic of this article we are going to talk about the new baby born in MakarenaLabs, Pablo, a custom-made robot which is able to recognize and detect people and objects in the surrounding environment.

In short, the aim of Pablo is to showcase how to construct a robotic system from scratch by using MakarenaLabs MuseBox, IP libraries and AMD’s (former Xilinx) FPGAs.

In conclusion of the article we will

Introduction to Musebox Certification

Musebox Certification from Xilinx is here! But what Musebox is? It is a real-time machine learning system which runs on FPGAs using Deep Learning Processing Unit (DPU) from Xilinx.

Musebox currently supports 5 different certification tasks:

Face Analysis
People Analysis
Audio Analysis
Object Analysis
Medical Analysis

In detail, Musebox offers 24 pre-trained AI models to solve the 5 tasks previously detailed. Those AI models can be re-trained in order to solve slightly different