ABSTRACT

Model pruning, which eliminates redundant parameters and reduces computational complexity, emerges as a viable strat- egy for efficient deep neural network (DNN) deployment. Owing to the irregular memory access and computation pat- terns in the sparse DNN models after pruning, existing arts have suggested various structured sparse patterns to enhance sparse DNN performance. In this work, we propose a unique perspective of understanding existing sparse pattern design as computation-skipping after tiling the tensor computa- tion into multi-level hierarchies. This unified perspective opens up a new design space of multi-level sparse tiling to maximize the sparsity benefits of DNNs, as opposed to the single-level choice in current practices. We present Fractal, an auto-tuning system for sparse patterns that identifies the optimal multi-level sparse tiling pattern. We introduce Pat- ternIR, a novel high-level intermediate representation (IR), to express a diverse range of multi-level sparse patterns. By leveraging insights from prior dense operator optimizations, we translate PatternIR into low-level compiler IRs, facili- tating further operator optimization and code generation. Our evaluations demonstrate that Fractal yields substantial speedups of up to on average 3.16× on CUDA Core, 2.52× on TensorCore of GPUs compared to the state-of-art dense base- line under 75% sparsity while upholding minimal accuracy degradation compared to prior sparse operator libraries.