CLP-SNN matches replay-based accuracy rehearsal-free on OpenLORIS few-shot continual learning and achieves 113x lower latency plus 6600x lower energy on Loihi 2 than edge-GPU baselines through algorithmic efficiency and neuromorphic hardware co-design.
Continual learn- ing at the edge: Real-time training on smartphone devices
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HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.
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Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network
CLP-SNN matches replay-based accuracy rehearsal-free on OpenLORIS few-shot continual learning and achieves 113x lower latency plus 6600x lower energy on Loihi 2 than edge-GPU baselines through algorithmic efficiency and neuromorphic hardware co-design.
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HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
HEDP uses energy regularization inspired by Helmholtz free energy plus hybrid energy-distance weighting in prompts to improve domain selection and achieve a 2.57% accuracy gain on benchmarks like CORe50 while mitigating catastrophic forgetting.