SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity

• URL: http://arxiv.org/abs/2501.15448v1
• Date: Sun, 26 Jan 2025 08:34:26 GMT
• Title: SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity
• Authors: Zichen Fan, Steve Dai, Rangharajan Venkatesan, Dennis Sylvester, Brucek Khailany,
• Abstract summary: We present a novel diffusion model accelerator featuring a mixed-precision dense-sparse architecture, channel-last address mapping, and a time-step-aware sparsity detector.<n>Our accelerator achieves 6.91x speed-up and 51.5% energy reduction compared to traditional dense accelerators.
• Score: 4.6126713437495495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have gained significant popularity in image generation tasks. However, generating high-quality content remains notably slow because it requires running model inference over many time steps. To accelerate these models, we propose to aggressively quantize both weights and activations, while simultaneously promoting significant activation sparsity. We further observe that the stated sparsity pattern varies among different channels and evolves across time steps. To support this quantization and sparsity scheme, we present a novel diffusion model accelerator featuring a heterogeneous mixed-precision dense-sparse architecture, channel-last address mapping, and a time-step-aware sparsity detector for efficient handling of the sparsity pattern. Our 4-bit quantization technique demonstrates superior generation quality compared to existing 4-bit methods. Our custom accelerator achieves 6.91x speed-up and 51.5% energy reduction compared to traditional dense accelerators.

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