Rethinking Control Flow in Spatial Architectures: Insights Into Control Flow Plane Design

Jinyi Deng, Xinru Tang, Jiahao Zhang, Yuxuan Li, Linyun Zhang, Fengbin Tu, Shaojun Wei, Yang Hu, Shouyi Yin

Abstract: Spatial architecture is a high-performance paradigm that employs control flow graphs and data flow graphs as computation model, and producer/consumer models as execution model. However, existing spatial architectures struggle with control flow handling challenges. Upon thoroughly characterizing their PE execution models, we observe that they lack autonomous, peer-to-peer, and temporally loosely-coupled control flow handling capability. This degrades its performance in intensive control programs. To tackle the existing control flow handling challenges, Marionette, a spatial architecture with an explicit-designed control flow plane, is proposed. We elaborately develop a full stack of Marionette architecture, from ISA, compiler, simulator to RTL. Marionette's flexible Control Flow Plane enables autonomous, peer-to-peer, and temporally loosely-coupled control flow management. Its Proactive PE Configuration ensures computation-overlapped and timely configuration to promote Branch Divergence handling capability. Besides, Marionette's Agile PE Assignment improves pipeline performance of imperfect loops. Compared to state-of-the-art spatial architectures, the experimental results demonstrate that Marionette outperforms Softbrain, TIA, REVEL, and RipTide by geomean 2.88$\mathbf{\times}$, 3.38$\mathbf{\times}$, 1.55$\mathbf{\times}$, and 2.66$\mathbf{\times}$ in a variety of challenging intensive control programs.