While research in the field of transformer models has primarily focused on enhancing performance metrics such as accuracy and perplexity, practical applications in industry often necessitate a rigorous consideration of inference latency constraints. Addressing this challenge, we introduce SpeedLimit, a novel Neural Architecture Search (NAS) technique that optimizes accuracy whilst adhering to an upper-bound latency constraint. Our method incorporates 8-bit integer quantization in the search process to outperform the current state-of-the-art technique. Our results underline the feasibility and efficacy of seeking an optimal balance between performance and latency, providing new avenues for deploying state-of-the-art transformer models in latency-sensitive environments.

翻译：尽管Transformer模型的研究主要聚焦于提升准确率与困惑度等性能指标，但工业界的实际应用往往需要严格考虑推理延迟约束。针对这一挑战，我们提出SpeedLimit——一种在满足上界延迟约束的同时优化准确率的新型神经架构搜索（NAS）技术。该方法在搜索过程中引入8位整数量化，以超越当前最先进技术。我们的结果凸显了在性能与延迟之间寻求最优平衡的可行性与有效性，为在延迟敏感环境中部署前沿Transformer模型开辟了新途径。