Lossless Acceleration of Large Language Models with Hierarchical
  Drafting based on Temporal Locality in Speculative Decoding
Authors: name: Sukmin Cho, name: Sangjin Choi, name: Taeho Hwang, name: Jeongyeon Seo, name: Soyeong Jeong, name: Huije Lee, name: Hoyun Song, name: Jong C. Park, name: Youngjin Kwon

Accelerating inference in Large Language Models (LLMs) is critical for
real-time interactions, as they have been widely incorporated into real-world
services. Speculative decoding, a fully algorithmic solution, has gained
attention for improving inference speed by drafting and verifying tokens,
thereby generating multiple tokens in a single forward pass. However, current
drafting strategies usually require significant fine-tuning or have
inconsistent performance across tasks. To address these challenges, we propose
Hierarchy Drafting (HD), a novel lossless drafting approach that organizes
various token sources into multiple databases in a hierarchical framework based
on temporal locality. In the drafting step, HD sequentially accesses multiple
databases to obtain draft tokens from the highest to the lowest locality,
ensuring consistent acceleration across diverse tasks and minimizing drafting
latency. Our experiments on Spec-Bench using LLMs with 7B and 13B parameters
demonstrate that HD outperforms existing database drafting methods, achieving
robust inference speedups across model sizes, tasks, and temperatures.