✨AutoNeural: Co-Designing Vision-Language Models for NPU Inference
📝 Summary:
AutoNeural is an NPU-native VLM co-designed for efficient edge inference. It uses a MobileNetV5-style vision backbone for stable integer quantization and a hybrid SSM-Transformer language backbone. This design reduces quantization errors and latency, improving real-time performance on edge devices.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02924
• PDF: https://arxiv.org/pdf/2512.02924
🔹 Models citing this paper:
• https://huggingface.co/NexaAI/AutoNeural
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For more data science resources:
✓ https://t.me/DataScienceT
#AutoNeural #VisionLanguageModels #EdgeAI #AIHardware #EfficientAI
📝 Summary:
AutoNeural is an NPU-native VLM co-designed for efficient edge inference. It uses a MobileNetV5-style vision backbone for stable integer quantization and a hybrid SSM-Transformer language backbone. This design reduces quantization errors and latency, improving real-time performance on edge devices.
🔹 Publication Date: Published on Dec 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02924
• PDF: https://arxiv.org/pdf/2512.02924
🔹 Models citing this paper:
• https://huggingface.co/NexaAI/AutoNeural
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#AutoNeural #VisionLanguageModels #EdgeAI #AIHardware #EfficientAI