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 only when meaningful changes occur. This could be highly advantageous, as current AI systems are known to consume large amounts of energy.
While some SNNs introduced in the past achieved encouraging results, they typically struggle to retain useful information (i.e., context) for long periods. This was found to be particularly challenging when the models have only a limited amount of data storage available or are operating under energy constraints.
Researchers at Imperial College London and ETH Zurich recently introduced new co-designed hardware and software that could overcome this limitation of SNNs. Their proposed architecture, introduced in a paper published in Nature Machine Intelligence, was found to tackle long-sequence tasks both effectively and energy-efficiently while also reducing data storage requirements.
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