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A Fully Quantized Training Accelerator for Diffusion Network With Tensor Type & Noise Strength Aware Precision Scheduling
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DOI：10.1109/TCSII.2024.3439319.png)
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Abstract
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Fine-grained mixed-precision fully-quantized methods have great potential to accelerate neural network training, but existing methods exhibit large accuracy loss for more complex models such as diffusion networks. This brief introduces a fully-quantized training accelerator for diffusion networks. It features a novel training framework with tensor-type- and noise-strength-aware precision scheduling to optimize bit-width allocation. The processing cluster design enables dynamical switching bit-width mappings for model weights, allows concurrent processing in 4 different bit-widths, and incorporates a gradient square sum collection unit to minimize on-chip memory access. Experimental results show up to 2.4x training speedup and 81% operation bit-width overhead reduction compared to existing designs, with minimal impact on image generation quality.
Keywords:
Fully-quantized training
mixed-precision quantization
diffusion network
neural network training accelerator
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