TileFlow: A Framework for Modeling Fusion Dataflow via Tree-based Analysis

With the increasing size of DNN models and the growing discrepancy between compute performance and memory bandwidth, fusing multiple layers together to reduce off-chip memory access has become a popular approach in dataflow design. However, designing such dataflows requires flexible and accurate performance models to facilitate evaluation, architecture analysis, and design space exploration. Unfortunately, current state-of-the-art performance models are limited to the dataflows of single operator acceleration, making them inapplicable to operator fusion dataflows.In this paper, we propose a framework called TileFlow that models dataflows for operator fusion. We first characterize the design space of fusion dataflows as a 3D space encompassing compute ordering, resource binding, and loop tiling. We then introduce a tile-centric notation to express dataflow designs within this space. Inspired by the tiling structure of fusion dataflows, we present a tree-based approach to analyze two critical performance metrics: data movement volume within the accelerator memory hierarchy and accelerator compute/memory resource usage. Finally, we leverage these metrics to calculate latency and energy consumption. Our evaluation validates TileFlow’s modeling accuracy against both real hardware and state-of-the-art performance models. We use TileFlow to aid in fusion dataflow design and analysis, and it helps us discover fusion dataflows that achieve an average runtime speedup of 1.85× for self-attention and 1.28× for convolution chains compared to the state-of-the-art dataflow.CCS CONCEPTS• Computing methodologies → Machine learning algorithms; • Hardware → Power estimation and optimization; Analysis and design of emerging devices and systems.