MASQ: Accelerating Masked Diffusion via Stage-Wise Multi-Precision Quantization
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Abstract (arXiv)
Masked diffusion enables region-specific image synthesis but suffers from computational redundancy, since the entire image is processed each timestep even though only the masked region requires generation. To address this, we introduce MASQ, a hardware-software co-designed accelerator for masked diffusion. Our approach performs stage-wise MXINT8/4/2 precision assignment that dynamically reflects spatial and semantic importance, complemented by timestep-aware scheduling and optimized non-matrix operations. MASQ features a block-wise multi-precision compute engine and mask management unit, efficiently handling our approach. It achieves up to 16.06x and 5.39x speedup and 4.18x and 4.93x energy-efficiency gain over A100 and Orin NX, respectively, while preserving quality.
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References · 15
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
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- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
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- 2019Data-Free Quantization Through Weight Equalization and Bias Correctioncited by 336
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