On-chip learning refers to the capability of neuromorphic hardware to adapt and learn directly on the chip itself, enabling real-time processing and adjustment of neural networks without needing external computation. This feature allows systems to be more efficient and responsive by integrating learning algorithms within the physical architecture, mimicking the biological processes of the brain. It enhances performance by reducing latency and energy consumption typically associated with transferring data to and from external memory.
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