Surrogate gradients for analog neuromorphic computing
PulseAugur coverage of Surrogate gradients for analog neuromorphic computing — every cluster mentioning Surrogate gradients for analog neuromorphic computing across labs, papers, and developer communities, ranked by signal.
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New method probes privacy risks in memristive AI hardware
Researchers have developed a new method called Spiking Domain Model Inversion (SDMI) to analyze privacy leakage in memristive spiking neural networks (SNNs). This technique combines Expectation over Transformation (EOT)…
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New SNN training and pruning methods boost efficiency and performance
Researchers are developing new methods to improve the efficiency and performance of Spiking Neural Networks (SNNs). One approach, Criticality-Constrained Quadratic Pruning (CQP), uses a combination of weight magnitude a…
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New SNN Training Methods Enhance Performance and Speed
Researchers are advancing Spiking Neural Networks (SNNs) through novel training methods and neuron models. One paper introduces a "circulate-firing" neuron and a learnable surrogate gradient function to improve informat…