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

Convolution Properties II01:17

Convolution Properties II

448
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
448
Convolution Properties I01:20

Convolution Properties I

379
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
379
Parallel Processing01:20

Parallel Processing

444
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...
444
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

673
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
673
Deconvolution01:20

Deconvolution

418
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
418
Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K

You might also read

Related Articles

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

Sort by
Same author

Nonlinear periodic orbit solutions and their bifurcation structure at the origin of soliton hopping in coupled microresonators.

Communications physics·2026
Same author

High-pulse-energy integrated mode-locked laser using a Mamyshev oscillator.

Nature·2026
Same author

Localized carbon deposition enables trimming of photonic integrated circuits.

Nature communications·2026
Same author

Nonvolatile photonic field-programmable coupler array.

Science advances·2026
Same author

Deep neural network inference on an integrated, reconfigurable photonic tensor processor.

Nature communications·2026
Same author

Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.

Nature communications·2026
Same journal

Incoming US science academy chief vows to 'double down' on research.

Nature·2026
Same journal

Author Correction: Synthesis of enantioenriched atropisomers by biocatalytic deracemization.

Nature·2026
Same journal

Electrodeposited self-assembled molecules for perovskite photovoltaics.

Nature·2026
Same journal

Neutrino's nursery found: the 'Shadow Blaster'.

Nature·2026
Same journal

Dementia risk in middle-aged people linked to a blood protein.

Nature·2026
Same journal

Daily briefing: What's really happening with trust in science.

Nature·2026
See all related articles

Related Experiment Video

Updated: Nov 22, 2025

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.8K

Parallel convolutional processing using an integrated photonic tensor core.

J Feldmann1, N Youngblood2,3, M Karpov4

  • 1Institute of Physics, University of Münster, Münster, Germany.

Nature
|January 7, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a photonic tensor core, an optical hardware accelerator, performing trillions of multiply-accumulate operations per second. This integrated photonic device offers a path towards faster, scalable AI hardware for data-intensive applications.

More Related Videos

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.2K
A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions
10:21

A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions

Published on: March 3, 2023

2.0K

Related Experiment Videos

Last Updated: Nov 22, 2025

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.8K
High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.2K
A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions
10:21

A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions

Published on: March 3, 2023

2.0K

Area of Science:

  • Integrated Photonics
  • Optical Computing
  • Artificial Intelligence Hardware

Background:

  • Exponential data growth from mobile networks, IoT, and AI necessitates faster, efficient hardware.
  • Current hardware limitations in speed and scalability for processing massive datasets.
  • Need for specialized hardware accelerators for computationally intensive AI tasks.

Purpose of the Study:

  • To demonstrate a computationally specific integrated photonic hardware accelerator (tensor core).
  • To achieve high-speed, parallelized in-memory computing using photonic technologies.
  • To explore the potential of integrated photonics for future AI hardware.

Main Methods:

  • Developed a photonic tensor core utilizing phase-change-material memory arrays.
  • Employed photonic chip-based optical frequency combs (soliton microcombs) for computation.
  • Reduced computation to measuring optical transmission through reconfigurable passive components.

Main Results:

  • Achieved operational speeds of trillions of multiply-accumulate operations per second (tera-MACs/s).
  • Demonstrated computation bandwidth exceeding 14 gigahertz, limited by modulator and photodetector speeds.
  • Showcased a pathway towards CMOS wafer-scale integration of the photonic tensor core.

Conclusions:

  • The photonic tensor core represents a significant advancement in optical computing hardware.
  • Integrated photonics offers a promising solution for parallel, fast, and efficient AI computation.
  • The technology has potential applications in autonomous driving, live video processing, and cloud computing.