A programmable superconducting LIF neuron with intrinsic static memory and dual-timescale plasticity achieves 45 GHz operation and femtojoule energy per spike.
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AutoFLIP prunes federated models via one-time collective loss-landscape mapping and client-agreement-guided adaptation, reporting 52% lower computation and 65% lower communication with SOTA non-IID accuracy.
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Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing
A programmable superconducting LIF neuron with intrinsic static memory and dual-timescale plasticity achieves 45 GHz operation and femtojoule energy per spike.
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Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
AutoFLIP prunes federated models via one-time collective loss-landscape mapping and client-agreement-guided adaptation, reporting 52% lower computation and 65% lower communication with SOTA non-IID accuracy.