Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parallel Processing01:20

Parallel Processing

908
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
908
Multimachine Stability01:25

Multimachine Stability

626
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
626
Cluster Sampling Method01:20

Cluster Sampling Method

15.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.7K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.4K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.4K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Noniterative Supervised On-Chip Training Circuitry for Reservoir Computing Systems.

IEEE transactions on neural networks and learning systems·2022
Same author

Fully Parallel Stochastic Computing Hardware Implementation of Convolutional Neural Networks for Edge Computing Applications.

IEEE transactions on neural networks and learning systems·2022
Same author

Climate change and their impacts in the Balearic Islands: a guide for policy design in Mediterranean regions.

Regional environmental change·2021
Same author

A global multinational survey of cefotaxime-resistant coliforms in urban wastewater treatment plants.

Environment international·2020
Same author

Compact Hardware Synthesis of Stochastic Spiking Neural Networks.

International journal of neural systems·2019
Same author

An improved methodology to compute surface site interaction points using high density molecular electrostatic potential surfaces.

Journal of computational chemistry·2018

Related Experiment Video

Updated: Apr 12, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Ultra-fast data-mining hardware architecture based on stochastic computing.

Antoni Morro1, Vincent Canals1, Antoni Oliver1

  • 1Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain.

Plos One
|May 9, 2015
PubMed
Summary

Stochastic computing offers efficient hardware for analyzing large datasets. This new approach speeds up pattern recognition in huge databases by 7x using less hardware.

More Related Videos

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.8K
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.7K

Related Experiment Videos

Last Updated: Apr 12, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.8K
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.7K

Area of Science:

  • Computer Engineering
  • Data Science
  • Hardware Acceleration

Background:

  • The demand for efficient processing of large datasets necessitates novel hardware solutions.
  • Traditional digital implementations face limitations in speed and resource utilization for big data analysis.

Purpose of the Study:

  • To explore the application of stochastic computing for probabilistic pattern recognition in massive databases.
  • To propose and evaluate a hardware architecture for efficient similarity search.

Main Methods:

  • Implementing a parallel architecture using pulse-based stochastic-logic blocks.
  • Conducting a similarity search against pre-stored categories within large datasets.
  • Comparing the performance against conventional digital implementations on identical hardware.

Main Results:

  • The proposed stochastic computing architecture significantly accelerates database screening.
  • A speed-up factor of 7 was achieved compared to conventional digital methods.
  • The system demonstrates efficiency in terms of hardware area utilization.

Conclusions:

  • Stochastic computing provides a viable and efficient solution for pattern recognition in large-scale data.
  • The developed pulse-based architecture offers substantial performance gains for big data analysis.
  • This approach paves the way for more powerful and resource-efficient data processing systems.