Ladder: Enabling Efficient Low-Precision Deep Learning Computing through Hardware-aware Tensor Transformation

The increasing demand for improving deep learning model performance has led to a paradigm shift in supporting low-precision computation to harness the robustness of deep learning to errors. Despite the emergence of new low-precision data types and optimization approaches, existing hardware and software have insufficient and inefficient support for those evolving data types, making it challenging to achieve real performance gains through low-precision computing. This paper introduces Ladder, a novel compiler designed to bridge the gap between evolving custom data types and the fixed precision formats supported by current hardware. Leveraging a general type system, tType, and an extended tensor expression, Ladder transforms deep neural network (DNN) computations into optimized computing pipelines with custom data types as the first-class citizen, exposing an optimization space for efficiently handling data storage, accesses, and type conversions. Ladder employs a new set of tensor scheduling primitives and a hardware-aware optimization policy to navigate the complex transformation space, ensuring optimal performance across different memory layers and DNN operators.