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Vision SmolMamba uses spike-guided pruning for energy-efficient vision models

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Vision SmolMamba, a novel energy-efficient spiking state-space architecture designed for visual modeling. This architecture integrates spike-driven dynamics with linear-time selective recurrence, utilizing a Spike-Guided Spatio-Temporal Token Pruner (SST-TP) to estimate token importance based on spike activation and latency. By progressively removing redundant tokens, Vision SmolMamba preserves crucial spatio-temporal information, enabling efficient scaling and improved accuracy-efficiency trade-offs. Experiments on various benchmarks show it reduces energy costs by at least 1.5x compared to previous spiking Transformer and Mamba variants. AI

IMPACT Introduces a more energy-efficient approach to spiking neural networks for vision tasks, potentially reducing computational costs.

RANK_REASON Academic paper introducing a new model architecture and pruning technique.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Vision SmolMamba uses spike-guided pruning for energy-efficient vision models

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Academic paper introducing a new model architecture and pruning technique.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Dewei Bai, Hongxiang Peng, Yunyun Zeng, Ziyu Zhang, Hong Qu, Yi Zhang ·

    Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models

    arXiv:2604.25570v1 Announce Type: new Abstract: Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token interactions remain fundamentally misaligned with the sparse and event-driven nature…

  2. arXiv cs.CV TIER_1 English(EN) · Yi Zhang ·

    Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models

    Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token interactions remain fundamentally misaligned with the sparse and event-driven nature of spiking neural computation. To address this …

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