DynaTE: Accelerating Diffusion LLMs via Dynamic Token Execution
Diffusion-based LLMs (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs by enabling bidirectional parallel refinement, alleviating the sequential decoding bottleneck of AR generation. However, their parallel iterative refinement mismatches AR accelerators optimized for sequential decoding and their discrete token generation differs from DiT accelerators designed for continuous denoising. Recent dLLM accelerators have explored workload-specific optimizations to reduce vocabulary processing overhead and redundant computation across denoising iterations. However, these approaches retain all tokens in parallel execution, despite varying token refinement utili...
| Authors | Minghan Jiang, Jiayi Wang, Shuaiting Li et al. |
|---|---|
| Primary category | cs.AR |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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