预测流水线解码：用于精确LLM解码的计算-延迟权衡
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding
AI总结：
本文提出预测流水线解码（PPD）方法，通过额外计算资源并行化后续token解码以加速LLM贪婪解码，并建立理论框架和初步实验验证其在降低延迟的同时保持输出完全一致。
摘要：
本文提出了“预测流水线解码（PPD）”，这是一种加速大型语言模型（LLM）贪婪解码的方法，同时保持与原始解码完全相同的输出。与传统策略不同，PPD利用额外的计算资源，在当前token解码期间并行化后续token解码的启动。该方法降低了解码延迟，并重塑了对LLM解码策略中权衡的理解。我们开发了一个理论框架，用于分析计算与延迟之间的权衡。利用该框架，我们可以分析性地估计与我们提出的方法相关的潜在延迟减少，这是通过评估表示为p_correct的匹配率来实现的。结果表明，使用额外的计算资源有潜力加速LLM解码。此外，我们实现了PPD并进行了初步实验，以经验性地验证其有效性，解决了理论分析未涵盖的潜在实际开销。
英文摘要：
This paper presents "Predictive Pipelined Decoding (PPD)," an approach that speeds up greedy decoding in Large Language Models (LLMs) while maintaining the exact same output as the original decoding. Unlike conventional strategies, PPD employs additional compute resources to parallelize the initiation of subsequent token decoding during the current token decoding. This method reduces decoding latency and reshapes the understanding of trade-offs in LLM decoding strategies. We have developed a theoretical framework that allows us to analyze the trade-off between computation and latency. Using this framework, we can analytically estimate the potential reduction in latency associated with our proposed method, achieved through the assessment of the match rate, represented as p_correct. The results demonstrate that the use of extra computational resources has the potential to accelerate LLM decoding. Additionally, we implement PPD and conduct preliminary experiments to empirically validate its efficacy, addressing potential practical overheads not covered by theoretical analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
