Towards Efficient Control Flow Handling in Spatial Architecture via Architecting the Control Flow Plane

TL;DR

This paper introduces Marionette, a spatial architecture with an explicit control flow plane that enhances control flow handling, leading to significant performance improvements in control-intensive programs.

Contribution

It proposes a novel control flow plane in spatial architecture, enabling autonomous, peer-to-peer, and loosely-coupled control flow management, which was lacking in prior designs.

Findings

Marionette outperforms state-of-the-art architectures in various control programs.

The architecture achieves up to 3.38x performance improvement.

Full stack implementation demonstrates practical viability.

Abstract

Spatial architecture is a high-performance architecture that uses control flow graphs and data flow graphs as the computational model and producer/consumer models as the execution models. However, existing spatial architectures suffer from control flow handling challenges. Upon categorizing their PE execution models, we find that they lack autonomous, peer-to-peer, and temporally loosely-coupled control flow handling capability. This leads to limited performance in intensive control programs. A spatial architecture, Marionette, is proposed, with an explicit-designed control flow plane. The Control Flow Plane enables autonomous, peer-to-peer and temporally loosely-coupled control flow handling. The Proactive PE Configuration ensures timely and computation-overlapped configuration to improve handling Branch Divergence. The Agile PE Assignment enhance the pipeline performance of Imperfect Loops. We develop full stack of Marionette (ISA, compiler, simulator, RTL) and demonstrate that in a variety of challenging intensive control programs, compared to state-of-the-art spatial architectures, Marionette outperforms Softbrain, TIA, REVEL, and RipTide by geomean 2.88x, 3.38x, 1.55x, and 2.66x.