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ResilPhase achieves high-fidelity diffusion model acceleration by eliminating derivative instability, setting a new standard for inference speed without sacrificing quality.
Even tiny hardware imperfections in compute-in-memory accelerators can trigger catastrophic failures in neural networks, demanding new co-design strategies.
