EGGROLL applies low-rank evolution strategies to train leaky integrate-and-fire spiking neural networks, reaching 79.21% accuracy on N-MNIST with 2.23 times lower per-generation time than full-rank ES.
Cohen, and Nitish Thakor
3 Pith papers cite this work. Polarity classification is still indexing.
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HyperSpace shows HRR and FHRR have comparable end-to-end runtime in spatial tasks despite FHRR's lower theoretical complexity per operation, with HRR using roughly half the memory.
SNN-HDC decoding delivers better accuracy, lower latency, and 1.24x-3.67x lower estimated energy than standard methods on DvsGesture and SL-Animals-DVS while detecting 100% of samples from an untrained class.
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Hyperdimensional Decoding of Spiking Neural Networks
SNN-HDC decoding delivers better accuracy, lower latency, and 1.24x-3.67x lower estimated energy than standard methods on DvsGesture and SL-Animals-DVS while detecting 100% of samples from an untrained class.