• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer

Analog IC Tips

Analog IC Design, Products, Tools Layout

  • Products
    • Amplifiers
    • Clocks & Timing
    • Data Converters
    • EMI/RFI
    • Interface & Isolation
    • MEMS & Sensors
  • Applications
    • Audio
    • Automotive/Transportation
    • Industrial
    • IoT
    • Medical
    • Telecommunications
    • Wireless
  • Learn
    • eBooks / Tech Tips
    • FAQs
    • EE Learning Center
    • EE Training Days
    • Tech Toolboxes
    • Webinars & Digital Events
  • Resources
    • Design Guide Library
    • Digital Issues
    • Engineering Diversity & Inclusion
    • LEAP Awards
    • Podcasts
    • White Papers
    • DesignFast
  • Video
    • EE Videos
    • Teardown Videos
  • EE Forums
    • EDABoard.com
    • Electro-Tech-Online.com
  • Engineering Training Days
  • Advertise
  • Subscribe

What is pseudorandomness and why is it useful?

February 2, 2021 By Jeff Shepard

Pseudorandomness is a measurement of the degree to which a sequence of numbers, though appearing to be random, is produced by a deterministic and repeatable process. True randomness is a stochastic quality of a sequence which has a probability distribution or pattern that may be analyzed statistically but may not be predicted precisely. Pseudorandom sequences are easier to produce than truly random sequences, and the seeming randomness is the basis for many security applications.

pseudorandomnessNot all “random” selections even qualify as pseudorandom. For example, systems that “randomly” sequence music tracks can appear to be random on first listen but often include restrictions such as not allowing the same item to appear two or more times in succession or selecting specific music types to follow each other.

Two principal methods are used to generate random and pseudorandom numbers, and a third method is emerging. The use of naturally-occurring physical phenomena can be used to produce the highest level of randomness. Atmospheric or thermal noise and other electromagnetic radiation sources, such as cosmic background radiation or radioactive decay, measured over short time periods can be used as entropy sources to make true random number generators (TRNGs).

Depending on the physical phenomena being measured, natural sources of entropy can be harvested at varying rates. Because of these rate limitations, it takes a finite block of time to harvest enough entropy for the process to proceed, and these sources are called “blocking.” As a result, TRNGs can be relatively slow, depending on the entropy source’s availability and/or strength.

Online services exist that use entropy sources to generate true random numbers. They offer both free and paid services. Random.org uses atmospheric noise as the basis of its random number generation. Radioactive source decays are completely unpredictable, and they are easy to detect, measure, and enter into a computer. That can avoid or at least minimize the “blocking” effect described above. The HotBits service from Fourmilab is an example of a TRNG based on radioactive decay.

Computational algorithms are a common method for producing long sequences of pseudorandom numbers. A shorter initial value completely determines the resulting sequences, called the seed value or key. If the key is known, pseudorandom sequences can be reproduced. Pseudorandom number generators (PRNGs) do not rely on natural entropy sources, but hybrid systems exist that periodically use natural entropy to increase the “randomness” of the process. PRNGs are non-blocking, are not rate-limited, and can produce varying strings of pseudorandom numbers on command.

A PRNG that is sufficiently random for security and encryption applications are called cryptographically secure pseudorandom number generators (CSPRNGs). Fortuna is an example of a CSPRNG. Fortuna generates cryptographically secure pseudorandom numbers on a computer. It can also be used as a TRNG, accepting random inputs from analog random sources. And like PRNGs, CSPRNGs can take a hybrid approach combining natural entropy (when available) with the basic computational algorithms.

Quantum random number generators (QRNGs) are an emerging area in pseudorandom number generation. QRNGs are being explored for use in artificial intelligence, Monte Carlo simulations, and sampling processes, as well as cryptography. While QRNGs are theoretically unpredictable, there are still challenges to implementation due to device and process imperfections. Similar to theoretically secure quantum key protocols, real QRNG implementations are limited in performance. For QRNGs, there are tradeoffs between a higher level of performance and reduced overall implementation efficiency.

QRNGs are expected to find near-term application in machine learning models such as neural networks and convolutional neural networks. They can be used for random initial weight distributions and for producing random splitting processes, enhancing machine learning compared with PRNGs.

There is a growing need for random and pseudorandom numbers in various applications ranging from security to artificial intelligence and machine learning. As a result, it is important to understand the available options and their performance differences.

You may also like:


  • Analog computation, Part 2: When and how
  • Analog computation
    Analog computation, Part 1: What and why

Filed Under: Applications, AR/VR, Artificial Intelligence, FAQ, Featured Tagged With: FAQ

Primary Sidebar

Featured Contributions

Design a circuit for ultra-low power sensor applications

Active baluns bridge the microwave and digital worlds

Managing design complexity and global collaboration with IP-centric design

PCB design best practices for ECAD/MCAD collaboration

Open RAN networks pass the time

More Featured Contributions

EE TECH TOOLBOX

“ee
Tech Toolbox: 5G Technology
This Tech Toolbox covers the basics of 5G technology plus a story about how engineers designed and built a prototype DSL router mostly from old cellphone parts. Download this first 5G/wired/wireless communications Tech Toolbox to learn more!

EE LEARNING CENTER

EE Learning Center
“analog
EXPAND YOUR KNOWLEDGE AND STAY CONNECTED
Get the latest info on technologies, tools and strategies for EE professionals.

EE ENGINEERING TRAINING DAYS

engineering

RSS Current EDABoard.com discussions

  • Diode recovery test Irrm timing.
  • How to make string LEDs?
  • The Analog Gods Hate Me
  • Battery Deep Discharge – IC Workarounds?
  • Safe Current and Power Density Limits in PCB Copper(in A/m² and W/m³) simulation

RSS Current Electro-Tech-Online.com Discussions

  • Raise your hand if your car had one of these:
  • Tektronix 2235 channel 1 trace unstable
  • How to make string LEDs?
  • Wideband matching an electrically short bowtie antenna; 50 ohm, 434 MHz
  • The Analog Gods Hate Me
“bills

Design Fast

Component Selection Made Simple.

Try it Today
design fast globle

Footer

Analog IC Tips

EE WORLD ONLINE NETWORK

  • 5G Technology World
  • EE World Online
  • Engineers Garage
  • Battery Power Tips
  • Connector Tips
  • DesignFast
  • EDA Board Forums
  • Electro Tech Online Forums
  • EV Engineering
  • Microcontroller Tips
  • Power Electronic Tips
  • Sensor Tips
  • Test and Measurement Tips

ANALOG IC TIPS

  • Subscribe to our newsletter
  • Advertise with us
  • Contact us
  • About us

Copyright © 2025 · WTWH Media LLC and its licensors. All rights reserved.
The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media.

Privacy Policy