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The robustness of autonomous inference-only devices deployed in the real world is limited by data distribution changes induced by different users, environments, and task requirements. This challenge calls for the development of edge devices with an always-on adaptation to their target ecosystems. However, the memory requirements of conventional neural-network training algorithms scale with the temporal depth of the data being processed, which is not compatible with the constrained power and area budgets at the edge. For this reason, previous works demonstrating end-to-end on-chip learning without external memory were restricted to the processing of static data such as images [1]&#x2013;[4], or to instantaneous decisions involving no memory of the past, e.g. obstacle avoidance in mobile robots [5]. The ability to learn short-to-long-term temporal dependencies on-chip is a missing enabler for robust autonomous edge devices in applications such as gesture recognition, speech processing, and cognitive robotics.

ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales