Inspired by the cerebellum, a Northwestern University chip detects anomalies, like irregular heartbeats, with 98% accuracy in milliseconds, using 10,000x fewer computing operations than standard AI, making it ideal for efficient wearables and autonomous systems.

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Northwestern University engineers built a brain-inspired chip that mimics the cerebellum, detecting unusual events while ignoring routine data. It identified abnormal heart rhythms in under a fifth of a heartbeat with 98% accuracy, using 10,000 times fewer computing operations than standard AI, promising far more efficient wearable and autonomous systems.

Brain-inspired hardware brings faster, low-power anomaly detection to AI systems

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