Given the increasing complexity of AI applications, traditional spatial
architectures frequently fall short. Our analysis identifies a pattern of
interconnected, multi-faceted tasks encompassing both AI and general
computational processes. In response, we have conceptualized "Orchestrated AI
Workflows," an approach that integrates various tasks with logic-driven
decisions into dynamic, sophisticated workflows. Specifically, we find that the
intrinsic Dual Dynamicity of Orchestrated AI Workflows, namely dynamic
execution times and frequencies of Task Blocks, can be effectively represented
using the Orchestrated Workflow Graph. Furthermore, the intrinsic Dual
Dynamicity poses challenges to existing spatial architecture, namely
Indiscriminate Resource Allocation, Reactive Load Rebalancing, and Contagious
PEA Idleness.
To overcome these challenges, we present Octopus, a scale-out spatial
architecture and a suite of advanced scheduling strategies optimized for
executing Orchestrated AI Workflows, such as the Discriminate Dual-Scheduling
Mechanism, Adaptive TBU Scheduling Strategy, and Proactive Cluster Scheduling
Strategy. Our evaluations demonstrate that Octopus significantly outperforms
traditional architectures in handling the dynamic demands of Orchestrated AI
Workflows, and possesses robust scalability in large scale hardware such as
wafer-scale chip.

给定了日益复杂的人工智能应用，我们通过分析发现了一种相互连接的、多方面任务的模式，涵盖了人工智能和一般计算过程。对此，我们提出了 “Orchestrated AI Workflows” 的概念，将各种任务与逻辑驱动的决策整合成动态而复杂的工作流程。同时，我们提出了 Octopus，一个针对执行 Orchestrated AI Workflows 而优化的可扩展的空间架构和一套先进的调度策略，它在处理动态需求方面显著优于传统架构，并在大规模硬件（如大规模芯片）上具有强大的可扩展性。