Machine Learning


[From Wikipedia] Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Actually the new solutions available for the Embedded Market allows customers to start using these solutions for ML directly on edge device by:

  • processing data and algorithms by using the local power computation (CPU, GPU, dedicated HW accelerators, FPGAs)
  • processing data and algortithms by using cloud services connected to the platform on the field

DAVE Embedded Systems is active in this field and ML is one of the main research interest with the aim to support customers adopting these features on their products and supporting the embedded design including these capabilities.

In the following there is a list of examples of studies DAVE Embedded Systems is currently working on:


The DAVE Embedded Systems' Know How:

SBCX-TN-005: Using TensorFlow to implement a Deep Learning image classifier based on Azure Custom Vision-generated model

MISC-TN-010: Using NXP eIQ Machine Learning Development Environment with Mito8M SoM

MISC-TN-011: Running an Azure-generated TensorFlow Lite model on Mito8M SoM using NXP eIQ

MISC-TN-015: Proof-of-Concept of an industrial, high-frame-rate video recording/streaming system

Video Youtube: 

Get a quote

Page 1 of 4
Personal information
Dave - Embedded Systems / Need help?
Dave - Embedded Systems / Dave Wiki
Dave - Embedded Systems / Newsletter

crediti: Representa