Latest Classification Technical Articles

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Neural Network Quantization: What Is It and How Does It Relate to TinyML?

This article will give a foundational understanding of quantization in the context of machine learning, specifically tiny machine learning (tinyML).

March 06, 2022 by Jake Hertz

What Is TinyML?

Learn about a subsection of machine learning (ML) called Tiny Machine Learning (TinyML), what it is, its applications, hardware and software requirements, and its benefits.

January 09, 2022 by Jake Hertz

Physically Unclonable Functions: Classification, Evaluation, and Tradeoffs in PUFs

In this article, we’ll take a deeper look at how PUFs are classified, the attributes that make a “good” PUF, and design tradeoffs.

July 18, 2021 by Jake Hertz

Increasing the Accessibility of Machine Learning at the Edge

"Edge intelligence" is becoming more accessible—even to those designers without formal data science training—as new hardware becomes available.

April 01, 2021 by Ali Osman Ors

Nine Factors to Consider for Board-Level Machine Vision Camera Integration

Leveraging board-level cameras offer a variety of benefits. To help identify the right mix of features and design elements, here are some factors to consider when selecting and designing in an embedded machine vision camera.

February 11, 2021 by Brian Cha, FLIR

Adding an ISP and Machine Learning Acceleration to the i.MX 8M Family

Learn how the i.MX 8M Plus applications processor enables edge computing, speeding up machine learning for a variety of applications including industrial tasks.

November 10, 2020 by Ben Eckermann, NXP Semiconductors

How to Implement Digit Recognition with TensorFlow Lite using an i.MX RT1060 Crossover MCU

This article looks at digit detection and recognition using MNIST eIQ as an example, which consists of several parts — the digit recognition is performed by a TensorFlow Lite model, and a GUI is used to increase the usability of the i.MX RT1060 device.

September 22, 2020 by David Piskula, NXP Semiconductors

An Example of Securing In-Cabin AI using TEE on a Secure FPGA SoC

This article discusses trusted execution environments — already used in a variety of connected devices — by showing how using TEE and an FPGA SoC can work in vehicle in-cabin AI.

July 28, 2020 by Katherine Hsu, Hex-Five Security

How to Build a Variational Autoencoder with TensorFlow

Learn the key parts of an autoencoder, how a variational autoencoder improves on it, and how to build and train a variational autoencoder using TensorFlow.

April 06, 2020 by Henry Ansah Fordjour

Understanding Local Minima in Neural-Network Training

This article discusses a complication that can prevent your Perceptron from achieving adequate classification accuracy.

February 06, 2020 by Robert Keim

Incorporating Bias Nodes Into Your Neural Network

This article shows you how to add bias values to a multilayer Perceptron implemented in a high-level programming language such as Python.

February 05, 2020 by Robert Keim

How to Increase the Accuracy of a Hidden Layer Neural Network

In this article, we’ll perform some classification experiments and gather data on the relationship between hidden-layer dimensionality and network performance.

February 04, 2020 by Robert Keim

How Many Hidden Layers and Hidden Nodes Does a Neural Network Need?

This article provides guidelines for configuring the hidden portion of a multilayer Perceptron.

January 31, 2020 by Robert Keim

Training Datasets for Neural Networks: How to Train and Validate a Python Neural Network

In this article, we’ll use Excel-generated samples to train a multilayer Perceptron, and then we’ll see how the network performs with validation samples.

January 30, 2020 by Robert Keim

Signal Processing Using Neural Networks: Validation in Neural Network Design

This article explains why validation is particularly important when we’re processing data using a neural network.

January 28, 2020 by Robert Keim

The Importance of Reliability Verification in AI/ML Processors

With the adoption of artificial intelligence and machine learning in a wide variety of applications, reliability verification of AI/ML processors is critical since failures can have major consequences for the validity and legitimacy of AI/ML technology.

January 21, 2020 by Neel Natekar, Mentor

How to Create a Multilayer Perceptron Neural Network in Python

This article takes you step by step through a Python program that will allow us to train a neural network and perform advanced classification.

January 19, 2020 by Robert Keim

Neural Network Architecture for a Python Implementation

This article discusses the Perceptron configuration that we will use for our experiments with neural-network training and classification, and we’ll also look at the related topic of bias nodes.

January 09, 2020 by Robert Keim

Understanding Training Formulas and Backpropagation for Multilayer Perceptrons

This article presents the equations that we use when performing weight-update computations, and we’ll also discuss the concept of backpropagation.

December 27, 2019 by Robert Keim

How to Train a Multilayer Perceptron Neural Network

We can greatly enhance the performance of a Perceptron by adding a layer of hidden nodes, but those hidden nodes also make training a bit more complicated.

December 26, 2019 by Robert Keim