Effectively Scheduling Computational Graphs of Deep Neural Networks toward Their Domain-Specific Accelerators.

Fully exploiting the computing power of an accelerator specialized for deep neural networks (DNNs) calls for the synergy between network and hardware architectures, but existing approaches partition a computational graph of DNN into multiple sub-graphs by abstracting away hardware architecture and assign resources to each sub-graph, not only producing redundant off-core data movements but also under-utilizing the hardware resources of a domain-specific architecture (DSA). This paper introduces a systematic approach for effectively scheduling DNN computational graphs on DSA platforms. By fully taking into account hardware architecture when partitioning a computational graph into coarse-grained sub-graphs, our work enables the synergy between network and hardware architectures, addressing several challenges of prior work: (1) it produces larger but fewer kernels, converting a large number of off-core data movements into on-core data exchanges; (2) it exploits the imbalanced memory usage distribution across DNN network architecture, better saturating the DSA memory hierarchy; (3) it enables across-layer instruction scheduling not studied before, further exploiting the parallelism across different specialized compute units. Results of seven DNN inference models on a DSA platform show that our work outperforms TVM and AStitch by 11.15x and 6.16x, respectively, and obtains throughput competitive to the vendor-crafted implementation. A case study on GPU also demonstrates that generating kernels for our sub-graphs can surpass CUTLASS with and without convolution fusion by 1.06x and 1.23x, respectively.