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

179
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...
179
Multimachine Stability01:25

Multimachine Stability

188
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:
188
Acceleration Vectors01:30

Acceleration Vectors

8.1K
In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
8.1K
Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

667
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...
667
Accelerators01:17

Accelerators

93
Accelerators in concrete serve as admixtures to speed up the hardening process, enabling the concrete to achieve early strength faster. Although accelerators do not necessarily impact the time it takes concrete to set, they reduce this time in practice. A common accelerator is calcium chloride, which is particularly useful for hastening early strength development in cold weather or for rapid repair jobs that require quick heat generation after mixing.
The effectiveness of calcium chloride can...
93

You might also read

Related Articles

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

Sort by
Same author

The neurobench framework for benchmarking neuromorphic computing algorithms and systems.

Nature communications·2025
Same author

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.

Nature communications·2024
Same author

E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware.

Frontiers in neuroscience·2022
Same author

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Frontiers in neuroscience·2022
Same author

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype.

IEEE transactions on biomedical circuits and systems·2019
Same author

Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype.

Frontiers in neuroscience·2018
Same journal

CEST MRI reveals nicotine-induced alterations in glutamate-associated molecular connectivity in the mouse brain.

Frontiers in neuroscience·2026
Same journal

Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.

Frontiers in neuroscience·2026
Same journal

Screening the optimal rTSMS frequency to orchestrate immune-fibrotic remodeling for adult spinal cord repair.

Frontiers in neuroscience·2026
Same journal

Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation.

Frontiers in neuroscience·2026
Same journal

Sex-divergent intrinsic brain function in Parkinson's disease: elevated nigral fluctuations and premotor-visuospatial coupling in female patients.

Frontiers in neuroscience·2026
Same journal

Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jul 18, 2025

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
08:28

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays

Published on: April 26, 2018

6.0K

Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2.

Jiaxin Huang1, Florian Kelber2, Bernhard Vogginger2

  • 1Infineon Technologies Dresden, Dresden, Germany.

Frontiers in Neuroscience
|August 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces parallel acceleration algorithms for spiking neural networks (SNNs) using SpiNNaker 2's MAC arrays. These novel algorithms significantly reduce memory footprint and execution time for SNN inference.

Keywords:
MAC arraySNNSpGEMMSpiNNaker 2multi-core load balancing deployment

More Related Videos

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.1K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

9.9K

Related Experiment Videos

Last Updated: Jul 18, 2025

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays
08:28

Assessment of the Effects of Endocrine Disrupting Compounds on the Development of Vertebrate Neural Network Function Using Multi-electrode Arrays

Published on: April 26, 2018

6.0K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.1K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

9.9K

Area of Science:

  • Neuromorphic computing
  • Artificial intelligence
  • Computer architecture

Background:

  • Spiking neural networks (SNNs) offer low-energy computation but face temporal challenges with large models.
  • Current CPU-based SNN processing is slow for extensive datasets and complex architectures.
  • Efficient hardware acceleration is crucial for realizing SNN potential.

Purpose of the Study:

  • To introduce parallel acceleration algorithms for SNN inference on SpiNNaker 2.
  • To investigate the integration of MAC arrays within processing elements (PEs) for enhanced SNN computation.
  • To develop and evaluate novel algorithms for spatio-temporal load balancing and performance optimization.

Main Methods:

  • Integration of the MAC array architecture into SpiNNaker 2's processing elements.
  • Development of parallel acceleration algorithms based on single-core optimization techniques.
  • Implementation of the Echelon Reorder model information densification algorithm.
  • Adaptation of multi-core two-stage splitting and authorization deployment strategies.
  • Benchmarking across diverse SNN models, including real-world applications and neuroscience models.

Main Results:

  • The echelon optimization algorithm achieved significant memory footprint reduction (74.28% and 85.78%) on tested SNN models.
  • Execution time was substantially reduced, accounting for ≤ 24.56% of the serial ARM baseline.
  • Efficient spatio-temporal load balancing and optimized performance were demonstrated.
  • The study confirmed the applicability of sparse matrix-matrix multiplication (SpGEMM) optimization to SNNs.

Conclusions:

  • The proposed parallel algorithms and MAC array integration offer efficient acceleration for SNN inference.
  • Novel SpGEMM optimization algorithms tailored for SNNs and MAC arrays are presented.
  • This work expands the application of SpGEMM to SNNs, enhancing neuromorphic hardware performance.