SpikeMLLM is the first spike-based MLLM framework that maintains near-lossless performance under aggressive timestep compression and delivers 9x throughput and 25x power efficiency gains via a custom RTL accelerator.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.NE 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.
A hybrid EWC+Replay spiking neural network achieves 0.979 average F1 score with near-zero forgetting on sequential nuclear ICS anomaly detection while using 12.6x fewer operations than an ANN.
citing papers explorer
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SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression
SpikeMLLM is the first spike-based MLLM framework that maintains near-lossless performance under aggressive timestep compression and delivers 9x throughput and 25x power efficiency gains via a custom RTL accelerator.
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Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.
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Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
A hybrid EWC+Replay spiking neural network achieves 0.979 average F1 score with near-zero forgetting on sequential nuclear ICS anomaly detection while using 12.6x fewer operations than an ANN.