Calculating the most efficient schedule of work in a neural network compiler is a difficult task. There are many parameters to be accounted for that can positively or adversely affect that schedule depending on their configuration - How work is shared between distributed targets, the subdivision of tensors to fit in memory, toggling the enablement of optimizations, etc. Traditionally, neural network compilers determine how to set these values by building a graph of choices and choosing the path with minimal 'cost'. These choices and their corresponding costs are usually determined by an algorithm crafted by engineers with a deep knowledge of the target platform. However, when the amount of options available to a compiler is large, it is very difficult to ensure that these models consistently produce an optimal schedule for all scenarios, whilst still completing compilation in an acceptable timeframe. This paper presents 'VPUNN' - a neural network-based cost model trained on low-level task profiling that consistently outperforms the state-of-the-art cost modeling in Intel's line of VPU processors.
翻译:在神经网络汇编器中计算最高效的工作时间表是一项艰巨的任务。 许多参数需要根据它们的配置来计算,这些参数可以积极或不利地影响这些时间表—— 分布目标之间如何分担工作, 分数组如何在记忆中相容, 以优化的功能等。 传统上, 神经网络汇编器通过绘制一个选择图和以最小的“ 成本” 选择路径来确定这些数值。 这些选择及其相应的成本通常由对目标平台有深入了解的工程师所设计的一种算法来决定。 但是, 当一个编译器的选项数量很大时, 很难确保这些模型始终为所有情景制作一个最佳的时间表, 同时仍然在可接受的时间框架内完成汇编工作。 本文介绍了“ VPUNN”, 这是一种基于神经网络的成本模型, 以低级别任务配置为模式, 持续超越了英特尔的VPU处理器系列中的最新成本建模。