Principle-based Dataflow Optimization for Communication Lower Bound in Operator-Fused Tensor Accelerator
Lei Xu, Chen Yin, Zelong Yuan, Weiguang Sheng, Jianfei Jiang, Qin Wang, Naifeng Jing · 2025
Although design space exploration (DSE) is good at finding dataflow for optimal memory access in tensor accelerators, it is very timing-consuming and lacks architecture insight. In this study, we for the first time propose several principles for dataflow optimization that provides lower bound of memory communication for tensor operators such as matrix multiplication. Through these principles we can calculate the best tiling, scheduling and mapping for both intra- and inter-operator dataflow. In addition, we can identify all the tensor-wise opertor fusion that are profitable in memory communication, so we propose FuseCU, a new architecture that supports these profitable fusion which can be applied to existing spatial architectures for data movement saving. Experimental results show that FuseCU delivers 63.6%, 62.4% and 38.7% data movement saving and $1.33 \times, 1.25 \times$ and $1.14 \times$ speedup compared to the TPUv4i, Gemmini and Planaria designs without increasing buffer size or bandwidth. Additionally, FuseCU is open-sourced.