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Reducing learning energy consumption is critical to edge-artificial intelligence (AI) processors with on-chip learning since on-chip learning energy dominates energy consumption, especially for applications that require long-term learning. To achieve this goal, we optimize a neuromorphic learning algorithm and propose random target window (TW) selection, hierarchical update skip (HUS), and asynchronous time step acceleration (ATSA) to reduce the on-chip learning power consumption. Our approach results in a 28-nm 1.25-mm2 asynchronous neuromorphic processor (ANP-I) with on-chip learning energy per sample less than 15&#x0025; of inference energy per sample. With all weights randomly initialized, this processor enables on-chip learning for edge-AI tasks such as gesture recognition, keyword spotting, and image classification, consuming sub-   $0.1 ~\mu \text{J}$    of learning energy per sample at 0.56 V and 40-MHz frequency while maintaining &#x003E;92&#x0025; accuracy for all tasks.

ANP-I: A 28-nm 1.5-pJ/SOP Asynchronous Spiking Neural Network Processor Enabling Sub-0.1- μ J/Sample On-Chip Learning for Edge-AI Applications