{"ID":2846689,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01553","arxiv_id":"2511.01553","title":"Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network","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.","short_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 fo...","url_abs":"https://arxiv.org/abs/2511.01553","url_pdf":"https://arxiv.org/pdf/2511.01553v2","authors":"[\"Elvin Hajizada\",\"Danielle Rager\",\"Timothy Shea\",\"Leobardo Campos-Macias\",\"Andreas Wild\",\"Eyke Hüllermeier\",\"Yulia Sandamirskaya\",\"Mike Davies\"]","published":"2025-11-03T13:16:16Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.DC\",\"cs.NE\"]","methods":"[]","has_code":false}