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arXiv · 2511.01553

Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network

Elvin Hajizada·Danielle Rager·Timothy Shea·Leobardo Campos-Macias·Andreas Wild·Eyke Hüllermeier·Yulia Sandamirskaya·Mike Davies
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Abstract

AI systems on edge devices require online continual learning -- adapting to non-stationary streams and unfamiliar classes without catastrophic forgetting -- under strict power constraints. We present CLP-SNN, a spiking neural network with a self-normalizing local learning rule and a spike-driven neural state machine for autonomous on-chip learning, implemented on Intel's Loihi 2 neuromorphic processor. On OpenLORIS few-shot experiments, CLP-SNN matches replay-based accuracy rehearsal-free. On Loihi 2, CLP-SNN achieves 113x lower latency (0.33 ms vs. 37.3 ms) and 6,600x lower energy (0.05 mJ vs. 333 mJ) than the strongest edge-GPU baseline. This gain decomposes into algorithmic efficiency (~14.5x latency, ~22.6x energy on the same GPU) and neuromorphic hardware co-design (~7.8x latency, ~295x energy) exploiting event-driven learning and sparse graded-spike communication. We show that co-designed brain-inspired algorithms and neuromorphic hardware can break traditional accuracy-efficiency trade-offs in edge AI.

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Elvin Hajizada, Danielle Rager, Timothy Shea, Leobardo Campos-Macias, Andreas Wild, Eyke Hüllermeier, Yulia Sandamirskaya, Mike Davies. 2025-11-03. Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network. https://arxiv.org/abs/2511.01553

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