Tensor computations overwhelm traditional general-purpose computing devices due to the large amounts of data and operations of the computations. They call for a holistic solution composed of both hardware acceleration and software mapping. Hardware/software (HW/SW) co-design optimizes the hardware and software in concert and produces high-quality solutions. There are two main challenges in the co-design flow. First, multiple methods exist to partition tensor computation and have different impacts on performance and energy efficiency. Besides, the hardware part must be implemented by the intrinsic functions of spatial accelerators. It is hard for programmers to identify and analyze the partitioning methods manually. Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge. The design space needs to be efficiently explored. To this end, we propose an agile co-design approach HASCO that provides an efficient HW/SW solution to dense tensor computation. We use tensor syntax trees as the unified IR, based on which we develop a two-step approach to identify partitioning methods. For each method, HASCO explores the hardware and software design spaces. We propose different algorithms for the explorations, as they have distinct objectives and evaluation costs. Concretely, we develop a multi-objective Bayesian optimization algorithm to explore hardware optimization. For software optimization, we use heuristic and Q-learning algorithms. Experiments demonstrate that HASCO achieves a 1.25X to 1.44X latency reduction through HW/SW co-design compared with developing the hardware and software separately.

中文翻译：

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HASCO：面向张量计算的敏捷硬件和软件协同设计

由于大量的数据和计算操作，张量计算使传统的通用计算设备不堪重负。他们呼吁采用由硬件加速和软件映射组成的整体解决方案。硬件/软件（HW / SW）协同设计可优化硬件和软件，并提供高质量的解决方案。协同设计流程中存在两个主要挑战。首先，存在多种划分张量计算的方法，这些方法对性能和能效具有不同的影响。此外，硬件部分必须通过空间加速器的固有功能来实现。程序员很难手动识别和分析分区方法。其次，由硬件/软件分区，硬件优化和软件优化组成的总体设计空间很大。需要有效地探索设计空间。为此，我们提出了一种敏捷的协同设计方法HASCO，它为密集的张量计算提供了有效的硬件/软件解决方案。我们使用张量语法树作为统一的IR，在此基础上我们开发了一种两步方法来识别分区方法。对于每种方法，HASCO都会探索硬件和软件设计空间。我们为勘探提出了不同的算法，因为它们具有不同的目标和评估成本。具体而言，我们开发了一种多目标贝叶斯优化算法来探索硬件优化。对于软件优化，我们使用启发式和Q学习算法。实验表明，与单独开发硬件和软件相比，HASCO通过HW / SW协同设计将延迟降低了1.25倍至1.44倍。