MASQ: Accelerating Masked Diffusion via Stage-Wise Multi-Precision Quantization

2026-05-22

AI Summary

This work addresses the computational redundancy in masked diffusion models, which denoise entire images during inference despite large masked regions. To overcome this inefficiency, the authors propose MASQ, a hardware-software co-designed accelerator architecture that integrates staged multi-precision quantization (MXINT8/4/2), timestep-aware scheduling, mask-aware computation, and customized non-matrix operation optimizations. MASQ introduces a novel strategy that jointly leverages spatial semantic importance and dynamic precision allocation, featuring a block-level multi-precision compute engine and a dedicated mask management unit. Experimental results demonstrate that MASQ achieves up to 16.06× speedup and 4.93× higher energy efficiency compared to NVIDIA A100 and Orin NX platforms, while preserving high-fidelity generation quality.

Abstract

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.

Problem

• masked diffusion
• computational redundancy
• region-specific image synthesis
• image generation

Innovation

• masked diffusion
• multi-precision quantization
• hardware-software co-design
• timestep-aware scheduling
• compute engine