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New quantization method boosts Mamba super-resolution speed by 2.23x

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new post-training quantization (PTQ) framework called Token-Group Quantization for Mamba-based super-resolution models. This method addresses the challenge of varying activation ranges across spatial tokens in Mamba models, which complicates low-bit quantization. The framework approximates a hardware-unfriendly grouping oracle by assigning tokens with similar clipping ranges to the same group, using a lightweight predictor to infer these groups from pre-scan features. Additionally, a Frequency-Preserving Refinement objective is introduced to maintain high-frequency structures during calibration, leading to improved performance and a 2.23x speedup on NVIDIA Jetson Orin Nano over FP16. AI

IMPACT This research could lead to more efficient deployment of Mamba-based models on edge devices, improving performance and reducing computational costs.

RANK_REASON The cluster contains a research paper detailing a novel method for optimizing Mamba models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New quantization method boosts Mamba super-resolution speed by 2.23x

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The cluster contains a research paper detailing a novel method for optimizing Mamba models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jinwoo Chung, Jangho Kim ·

    Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution

    arXiv:2610.16261v1 Announce Type: new Abstract: Mamba-based super-resolution models are efficient alternatives to Transformer-based restoration backbones, but their low-bit post-training quantization remains challenging because selective scan, Mamba's input-dependent state-space …

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