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arXiv · 2601.20706

NPU Design for Diffusion Language Model Inference

Binglei Lou·Haoran Wu·Kevin Lau·Gregor MacDonald·Jiayi Nie·Yao Lai·Can Xiao·Xuan Guo·Jianyi Cheng·Rika Antonova·Robert Mullins·Aaron Zhao
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Abstract

Diffusion-based LLMs (dLLMs) fundamentally depart from traditional autoregressive (AR) LLM inference: they leverage bidirectional attention, block-wise KV cache refreshing, cross-step reuse, and a non-GEMM-centric sampling phase. These characteristics make current dLLMs incompatible with most existing NPUs, as their inference patterns, in particular the reduction-heavy, top-$k$-driven sampling stage, demand new ISA and memory hierarchy support beyond that of AR accelerators. In addition, the blocked diffusion KV cache breaks from the append-only paradigm assumed by AR NPUs, and conventional AR-derived KV quantization schemes were designed for static activation distributions and do not account for the step-wise distribution shifts introduced by iterative block-wise refinement in dLLMs. In this paper, we introduce the first NPU accelerator specifically designed for dLLMs. It delivers: a dLLM-oriented ISA and compiler; a hardware-optimized execution model for both the transformer inference and diffusion sampling used in dLLMs; a novel Block-Adaptive Online Smoothing (BAOS) for quantizing KV cache in dLLMs; and a complete RTL implementation synthesized in 7nm. To evaluate and validate our design, we introduce a tri-path simulation framework that comprises analytical, cycle-accurate, and accuracy simulators, together with cross-validations against physical hardware. The full NPU stack, including ISA, simulation tools, and quantization software, will be open-sourced upon acceptance.

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Binglei Lou, Haoran Wu, Kevin Lau, Gregor MacDonald, Jiayi Nie, Yao Lai, Can Xiao, Xuan Guo, Jianyi Cheng, Rika Antonova, Robert Mullins, Aaron Zhao. 2026-04-23. NPU Design for Diffusion Language Model Inference. https://arxiv.org/abs/2601.20706

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