As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount. Frameworks, such as Halide and TVM, separate out the algorithmic representation of the network from the schedule that determines its implementation. Finding good schedules, however, remains extremely challenging. We model this scheduling problem as a sequence of optimization choices, and present a new technique to accurately predict the expected performance of a partial schedule. By leveraging these predictions we can make these optimization decisions greedily and rapidly identify an efficient schedule. This enables us to find schedules that improve the throughput of deep neural networks by 2.6x over Halide and 1.5x over TVM. Moreover, our technique is two to three orders of magnitude faster than that of these tools, and completes in seconds instead of hours.

中文翻译：

---

基于价值函数的深度学习工作负载性能优化

随着机器学习技术的普及，神经网络实现的效率相应地变得至关重要。诸如Halide和TVM之类的框架将网络的算法表示与确定其实现的时间表分开。然而，找到良好的时间表仍然极具挑战性。我们将此调度问题建模为一系列优化选择，并提出了一种新技术来准确预测部分调度的预期性能。通过利用这些预测，我们可以贪婪地做出这些优化决策，并快速确定有效的计划。这使我们能够找到时间表，将深度神经网络的吞吐量提高到Halide的2.6倍和TVM的1.5倍。此外，