Passive Components Blog
No Result
View All Result
  • Home
  • NewsFilter
    • All
    • Aerospace & Defence
    • Antenna
    • Applications
    • Automotive
    • Capacitors
    • Circuit Protection Devices
    • electro-mechanical news
    • Filters
    • Fuses
    • Inductors
    • Industrial
    • Integrated Passives
    • inter-connect news
    • Market & Supply Chain
    • Market Insights
    • Medical
    • Modelling and Simulation
    • New Materials & Supply
    • New Technologies
    • Non-linear Passives
    • Oscillators
    • Passive Sensors News
    • Resistors
    • RF & Microwave
    • Telecommunication
    • Weekly Digest

    Inductor Resonances and its Impact to EMI

    Developing Low Inductance Film Capacitor using Bode 100 Analyzer

    Highly Reliable Flex Rigid PCBs, Würth Elektronik Webinar

    Würth Elektronik Releases High Performance TLVR Coupled Inductors

    YAGEO Extends Rectangular Aluminum Electrolytic Capacitor Family

    Dr. Tomas Zednicek Vision for Europe 2025 Passive Electronics Market

    Littelfuse Releases Industry-First SMD Fuse with 1500A Interrupting Rating at 277V

    TDK Unveils Industry Highest Rated Current Multilayer Chip Beads

    Vishay Releases Automotive SMD Thick Film Power Resistor for Enhanced Protection Against Short Transient Pulses

    Trending Tags

    • Ripple Current
    • RF
    • Leakage Current
    • Tantalum vs Ceramic
    • Snubber
    • Low ESR
    • Feedthrough
    • Derating
    • Dielectric Constant
    • New Products
    • Market Reports
  • VideoFilter
    • All
    • Antenna videos
    • Capacitor videos
    • Circuit Protection Video
    • Filter videos
    • Fuse videos
    • Inductor videos
    • Inter-Connect Video
    • Non-linear passives videos
    • Oscillator videos
    • Passive sensors videos
    • Resistor videos

    Inductor Resonances and its Impact to EMI

    Highly Reliable Flex Rigid PCBs, Würth Elektronik Webinar

    Causes of Oscillations in Flyback Converters

    How to design a 60W Flyback Transformer

    Modeling and Simulation of Leakage Inductance

    Power Inductor Considerations for AI High Power Computing – Vishay Video

    Coupled Inductors in Multiphase Boost Converters

    VPG Demonstrates Precision Resistor in Cryogenic Conditions

    Comparison Testing of Chip Resistor Technologies Under High Vibration

    Trending Tags

    • Capacitors explained
    • Inductors explained
    • Resistors explained
    • Filters explained
    • Application Video Guidelines
    • EMC
    • New Products
    • Ripple Current
    • Simulation
    • Tantalum vs Ceramic
  • Knowledge Blog
  • Suppliers
    • Who is Who
  • Events
  • Home
  • NewsFilter
    • All
    • Aerospace & Defence
    • Antenna
    • Applications
    • Automotive
    • Capacitors
    • Circuit Protection Devices
    • electro-mechanical news
    • Filters
    • Fuses
    • Inductors
    • Industrial
    • Integrated Passives
    • inter-connect news
    • Market & Supply Chain
    • Market Insights
    • Medical
    • Modelling and Simulation
    • New Materials & Supply
    • New Technologies
    • Non-linear Passives
    • Oscillators
    • Passive Sensors News
    • Resistors
    • RF & Microwave
    • Telecommunication
    • Weekly Digest

    Inductor Resonances and its Impact to EMI

    Developing Low Inductance Film Capacitor using Bode 100 Analyzer

    Highly Reliable Flex Rigid PCBs, Würth Elektronik Webinar

    Würth Elektronik Releases High Performance TLVR Coupled Inductors

    YAGEO Extends Rectangular Aluminum Electrolytic Capacitor Family

    Dr. Tomas Zednicek Vision for Europe 2025 Passive Electronics Market

    Littelfuse Releases Industry-First SMD Fuse with 1500A Interrupting Rating at 277V

    TDK Unveils Industry Highest Rated Current Multilayer Chip Beads

    Vishay Releases Automotive SMD Thick Film Power Resistor for Enhanced Protection Against Short Transient Pulses

    Trending Tags

    • Ripple Current
    • RF
    • Leakage Current
    • Tantalum vs Ceramic
    • Snubber
    • Low ESR
    • Feedthrough
    • Derating
    • Dielectric Constant
    • New Products
    • Market Reports
  • VideoFilter
    • All
    • Antenna videos
    • Capacitor videos
    • Circuit Protection Video
    • Filter videos
    • Fuse videos
    • Inductor videos
    • Inter-Connect Video
    • Non-linear passives videos
    • Oscillator videos
    • Passive sensors videos
    • Resistor videos

    Inductor Resonances and its Impact to EMI

    Highly Reliable Flex Rigid PCBs, Würth Elektronik Webinar

    Causes of Oscillations in Flyback Converters

    How to design a 60W Flyback Transformer

    Modeling and Simulation of Leakage Inductance

    Power Inductor Considerations for AI High Power Computing – Vishay Video

    Coupled Inductors in Multiphase Boost Converters

    VPG Demonstrates Precision Resistor in Cryogenic Conditions

    Comparison Testing of Chip Resistor Technologies Under High Vibration

    Trending Tags

    • Capacitors explained
    • Inductors explained
    • Resistors explained
    • Filters explained
    • Application Video Guidelines
    • EMC
    • New Products
    • Ripple Current
    • Simulation
    • Tantalum vs Ceramic
  • Knowledge Blog
  • Suppliers
    • Who is Who
  • Events
No Result
View All Result
Passive Components Blog
No Result
View All Result

Memristors power quick-learning neural network

26.12.2017
Reading Time: 3 mins read
A A

source: Phys org news

A new type of neural network made with memristors can dramatically improve the efficiency of teaching machines to think like humans.

RelatedPosts

Inductor Resonances and its Impact to EMI

Developing Low Inductance Film Capacitor using Bode 100 Analyzer

Highly Reliable Flex Rigid PCBs, Würth Elektronik Webinar

The network, called a reservoir computing system, could predict words before they are said during conversation, and help predict future outcomes based on the present.

The research team that created the reservoir computing system, led by Wei Lu, professor of electrical engineering and computer science at the University of Michigan, recently published their work in Nature Communications.

Reservoir computing systems, which improve on a typical neural network’s capacity and reduce the required training time, have been created in the past with larger optical components. However, the U-M group created their system using memristors, which require less space and can be integrated more easily into existing silicon-based electronics.

Memristors are a special type of resistive device that can both perform logic and store data. This contrasts with typical computer systems, where processors perform logic separate from memory modules. In this study, Lu’s team used a special memristor that memorizes events only in the near history.

Inspired by brains, neural networks are composed of neurons, or nodes, and synapses, the connections between nodes.

To train a neural network for a task, a neural network takes in a large set of questions and the answers to those questions. In this process of what’s called supervised learning, the connections between nodes are weighted more heavily or lightly to minimize the amount of error in achieving the correct answer.

Once trained, a neural network can then be tested without knowing the answer. For example, a system can process a new photo and correctly identify a human face, because it has learned the features of human faces from other photos in its training set.

“A lot of times, it takes days or months to train a network,” Lu said. “It is very expensive.”

Image recognition is also a relatively simple problem, as it doesn’t require any information apart from a static image. More complex tasks, such as speech recognition, can depend highly on context and require neural networks to have knowledge of what has just occurred, or what has just been said.

 “When transcribing speech to text or translating languages, a word’s meaning and even pronunciation will differ depending on the previous syllables,” Lu said.

This requires a recurrent neural network, which incorporates loops within the network that give the network a memory effect. However, training these recurrent neural networks is especially expensive, Lu said.

Reservoir computing systems built with memristors, however, can skip most of the expensive training process and still provide the network the capability to remember. This is because the most critical component of the system—the reservoir—does not require training.

When a set of data is inputted into the reservoir, the reservoir identifies important time-related features of the data, and hands it off in a simpler format to a second network. This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.

“The beauty of reservoir computing is that while we design it, we don’t have to train it,” Lu said.

The team proved the reservoir computing concept using a test of handwriting recognition, a common benchmark among neural networks. Numerals were broken up into rows of pixels, and fed into the computer with voltages like Morse code, with zero volts for a dark pixel and a little over one volt for a white pixel.

Using only 88 memristors as nodes to identify handwritten versions of numerals, compared to a conventional network that would require thousands of nodes for the task, the reservoir achieved 91 percent accuracy.

Reservoir computing systems are especially adept at handling data that varies with time, like a stream of data or words, or a function depending on past results.

To demonstrate this, the team tested a complex function that depended on multiple past results, which is common in engineering fields. The reservoir computing system was able to model the complex function with minimal error.

Lu plans on exploring two future paths with this research: speech recognition and predictive analysis.

“We can make predictions on natural spoken language, so you don’t even have to say the full word,” Lu said. “We could actually predict what you plan to say next.”

In predictive analysis, Lu hopes to use the system to take in signals with noise, like static from far-off radio stations, and produce a cleaner stream of data.

“It could also predict and generate an output signal even if the input stopped,” he said.

 

 

featured image:

A nanocomponent that is capable of learning: The Bielefeld memristor built into a chip is 600 times thinner than a human hair. Credit: Bielefeld University

 

Related

Recent Posts

Highly Reliable Flex Rigid PCBs, Würth Elektronik Webinar

15.5.2025
14

Würth Elektronik Releases High Performance TLVR Coupled Inductors

15.5.2025
15

YAGEO Extends Rectangular Aluminum Electrolytic Capacitor Family

15.5.2025
24

Littelfuse Releases Industry-First SMD Fuse with 1500A Interrupting Rating at 277V

14.5.2025
7

TDK Unveils Industry Highest Rated Current Multilayer Chip Beads

14.5.2025
16

Vishay Releases Automotive SMD Thick Film Power Resistor for Enhanced Protection Against Short Transient Pulses

14.5.2025
8

Exxelia Power Film Capacitors Support Critical Systems Across Various Industries

13.5.2025
23

H2-Assisted Thermal Treatment of Electrode Materials Increases Supercapacitors Energy Density

13.5.2025
9

How to design a 60W Flyback Transformer

12.5.2025
27

Researchers Present Hybrid Supercapacitor Zn-Ion Microcapacitors

12.5.2025
25

Upcoming Events

May 28
16:00 - 17:00 CEST

Power Over Data Line

View Calendar

Popular Posts

  • Boost Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • Buck Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • Flyback Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • Why Low ESR Matters in Capacitor Design

    0 shares
    Share 0 Tweet 0
  • What is the Difference Between X8G, X8L and X8R Ceramic Capacitor Dielectrics?

    0 shares
    Share 0 Tweet 0
  • Ripple Current and its Effects on the Performance of Capacitors

    3 shares
    Share 3 Tweet 0
  • What is a Dielectric Constant and DF of Plastic Materials?

    4 shares
    Share 4 Tweet 0
  • SEPIC Converter Design and Calculation

    0 shares
    Share 0 Tweet 0
  • Supercapacitors Emerge as a Promising Solution to AI-Induced Power Energy Spikes

    0 shares
    Share 0 Tweet 0
  • Dual Active Bridge (DAB) Topology Explained

    0 shares
    Share 0 Tweet 0

Newsletter Subscription

 

Passive Components Blog

© EPCI - Leading Passive Components Educational and Information Site

  • Home
  • Privacy Policy
  • EPCI Membership & Advertisement
  • About

No Result
View All Result
  • Home
  • Knowledge Blog
  • Premium Suppliers

© EPCI - Leading Passive Components Educational and Information Site

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy and Cookie Policy.
Go to mobile version