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    Brain-inspired AI architecture could computing faster and far less power-hungry

    June 24, 2026, Categories  Physics, Technology/Electronics

    New brain-inspired architecture could process data more efficiently
    Dual memory pathway abstraction. At the algorithmic level, each layer maintains a shared, low-dimensional state that captures slow contextual dynamics and modulates fast spiking activity. At the hardware level, this separation is mirrored by a heterogeneous accelerator that keeps the compact state on-chip and fuses sparse and dense computations for efficient execution. Credit: Sun et al.

    Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with each other. While biological neurons exchange information in the form of electrical impulses, SNNs rely on brief signals known as spikes.

    SNNs have proved promising for reducing power consumption, as developers can ensure they do not process information continuously, but rather ...

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