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.
Training machine learning models at the edge: A survey
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UGEL employs deep beta regression to estimate uncertainty in one forward pass, enabling faster convergence in edge learning for remote sensing image regression than active or semi-supervised baselines.
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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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Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing
UGEL employs deep beta regression to estimate uncertainty in one forward pass, enabling faster convergence in edge learning for remote sensing image regression than active or semi-supervised baselines.