Asynchronous sequential updates in KLR Hopfield networks produce statistically indistinguishable trajectories from synchronous dynamics, achieve empirical capacities near P/N=30, and converge with event counts close to initial Hamming distance.
Loihi: A neuromorphic manycore processor with on-chip learning.IEEE Micro, 38(1):82–99
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A semantics-preserving co-design exports PyTorch SNNs to event-driven FPGAs via one artifact, achieving 87.4% MNIST accuracy identical to software reference at 0.1375 μs and 31.6 nJ per image.
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Efficient event-driven retrieval in high-capacity kernel Hopfield networks
Asynchronous sequential updates in KLR Hopfield networks produce statistically indistinguishable trajectories from synchronous dynamics, achieve empirical capacities near P/N=30, and converge with event counts close to initial Hamming distance.
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Hardware-Software Co-Design for Event-Driven SNN Deployment on Low-Cost Neuromorphic FPGAs
A semantics-preserving co-design exports PyTorch SNNs to event-driven FPGAs via one artifact, achieving 87.4% MNIST accuracy identical to software reference at 0.1375 μs and 31.6 nJ per image.