After completing the design and training phases, deploying a deep learning model onto specific hardware is essential before practical implementation. Targeted optimizations are necessary to enhance the model's performance by reducing inference latency. Auto-scheduling, an automated technique offering various optimization options, proves to be a viable solution for large-scale auto-deployment. However, the low-level code generated by auto-scheduling resembles hardware coding, potentially hindering human comprehension and impeding manual optimization efforts. In this ongoing study, we aim to develop an enhanced visualization that effectively addresses the extensive profiling metrics associated with auto-scheduling. This visualization will illuminate the intricate scheduling process, enabling further advancements in latency optimization through insights derived from the schedule.

翻译：完成设计和训练阶段后，将深度学习模型部署到特定硬件上是实际应用前的必要环节。为降低推理延迟以提升模型性能，需采用针对性优化策略。自动调度作为一种提供多种优化选项的自动化技术，已成为大规模自动部署的有效解决方案。然而，自动调度生成的底层代码类似于硬件编码，可能影响人类理解并阻碍手动优化进程。在本持续研究中，我们致力于开发一种增强型可视化工具，有效整合自动调度涉及的广泛性能指标。该可视化工具将揭示复杂的调度过程，从而通过调度中获得的洞察力推动延迟优化的进一步发展。