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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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
PAS-Net is a fully multiplier-free spiking neural network that enforces human joint constraints spatially and uses causal neuromodulation temporally to achieve state-of-the-art accuracy on IMU HAR with up to 98% lower dynamic energy via early-exit.
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.
ASN uses trainable parameters for adaptive membrane dynamics and firing in SNNs, with NASN adding normalization, and reports effectiveness across 19 vision and language datasets.
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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Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition
PAS-Net is a fully multiplier-free spiking neural network that enforces human joint constraints spatially and uses causal neuromodulation temporally to achieve state-of-the-art accuracy on IMU HAR with up to 98% lower dynamic energy via early-exit.
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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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Adaptive Spiking Neurons for Vision and Language Modeling
ASN uses trainable parameters for adaptive membrane dynamics and firing in SNNs, with NASN adding normalization, and reports effectiveness across 19 vision and language datasets.