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SSDi8 framework enhances Mamba-2 architecture with 8-bit quantization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SSDi8, a novel 8-bit quantization framework specifically for the Mamba-2 architecture's Structured State Space Duality (SSD). This method aims to reduce the memory and latency overhead introduced by Mamba-2's integration of recurrent and attention modes. SSDi8 achieves this by reformulating computations to allow reuse of quantized activations and adaptively quantizing channel-varying activations. Experiments show SSDi8 maintains accuracy comparable to FP16 while offering up to a 1.4x speedup in W4A8 and W8A8 settings, and has been successfully deployed on resource-constrained devices like the Orin NX. AI

IMPACT This quantization technique could enable more efficient deployment of advanced sequence models like Mamba-2 on edge devices.

RANK_REASON This is a research paper detailing a new technical method for model quantization. [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 →

SSDi8 framework enhances Mamba-2 architecture with 8-bit quantization

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This is a research paper detailing a new technical method for model quantization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hyunwoo Kim, Byoungchan Ko, Minseok Kang, Minwoo Kim, Dongjin Lee, Jaehoon Lee, Sungroh Yoon, Dahuin Jung ·

    SSDi8: Accurate and Efficient 8-bit Quantization for State Space Duality

    arXiv:2608.21952v1 Announce Type: new Abstract: Recent advances in sequence modeling have highlighted Mamba as a state space architecture offering efficient long-range dependency modeling and providing a viable alternative to Transformers. Building upon this, Mamba-2 introduces t…

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