LoopTree: Enabling Exploration of Fused-layer Dataflow Accelerators
Michael Gilbert, Yannan Nellie Wu, Angshuman Parashar, Vivienne Sze, Joel Emer · 2023
Many accelerators today process deep neural networks layer by layer. As a consequence of this processing style, every intermediate feature map incurs expensive off-chip transfers. Layer fusion eliminates off-chip transfers of intermediate results, leading to better latency and energy efficiency. Prior works have explored only subsets of the fused-layer design space, looking only at a particular choice of tiling, scheduling, and buffering strategy. Their architectural models are also tailored for their proposed datatlow. The lack of a unified, systematic representation of designs and a versatile evaluation method has prevented thorough exploration of the design space. To enable systematic exploration of this design space, we present LoopTree, a framework for describing and evaluating any design in our expanded fused-layer datatlow design space. With a case study, we explore new designs to show that exploring our larger design space uncovers more efficient designs, especially for recent workloads with diverse layer types. Our design achieves 2.5× speedup and 2× lower energy compared to an optimized layer-by-layer design. Compared to a state-of-the-art fused-layer design, we match latency and energy while using 25% less onchip buffer space.