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Related Concept Videos

Neural Circuits01:25

Neural Circuits

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.
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Parallel Processing

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...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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Automatic Processing and Automatic Social Behavior01:28

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Related Experiment Video

Updated: Jul 1, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

ANP-R: A 22nm 0.88pJ/SOP Asynchronous SNN-based Processor with Coarse-Grained Reconfigurable Architecture Enabling

Ziyi Cheng, Dexuan Huo, Jilin Zhang

    IEEE Transactions on Biomedical Circuits and Systems
    |June 29, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ANP-R, an energy-efficient Spiking Neural Network (SNN) processor for edge AI. It overcomes design trade-offs, enabling accurate multi-task learning with reduced power and memory usage.

    Related Experiment Videos

    Last Updated: Jul 1, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    Area of Science:

    • Neuromorphic Engineering
    • Artificial Intelligence
    • Edge Computing

    Background:

    • Spiking Neural Networks (SNNs) offer energy-efficient, brain-inspired computing for edge AI.
    • Existing SNN processors face a trade-off between low power/task-specific design and high accuracy/general-purpose design.
    • Continuous learning in dynamic edge environments necessitates efficient on-chip adaptation.

    Purpose of the Study:

    • To present ANP-R, a novel 22nm asynchronous SNN-based edge AI processor.
    • To enable one-shot, few-shot, batch, and incremental on-chip learning capabilities.
    • To overcome the power-accuracy-memory trade-off in current SNN edge processors.

    Main Methods:

    • Developed a 22nm asynchronous SNN processor (ANP-R) with a coarse-grained reconfigurable architecture.
    • Integrated 64 cores with 4096 neurons and 0.262 million synapses.
    • Implemented STDP-based SNN topologies and an energy-efficient asynchronous training method with adaptive weight updates and low-bit width coding.

    Main Results:

    • Achieved over 95% average accuracy across four sensory tasks using the reconfigurable architecture.
    • Reduced redundant synaptic weight updates by up to 65% and storage cost by up to 50% without significant accuracy loss.
    • Demonstrated high accuracies: 92.1% (gesture), 93.9% (keyword), 98.6% (object), 99.2% (gas).

    Conclusions:

    • ANP-R effectively addresses the limitations of existing SNN processors for edge AI.
    • The proposed architecture and training methods significantly improve energy efficiency, accuracy, and memory usage.
    • This work advances the development of practical, high-performance SNN-based edge AI solutions.