End-to-End DNN Training with Block Floating Point Arithmetic

DNNs are ubiquitous datacenter workloads, requiring orders of magnitude more computing power from servers than traditional workloads. As such, datacenter operators are forced to adopt domain-specific accelerators that employ half-precision floating-point (FP) numeric representations to improve arithmetic density. Unfortunately, even these representations are not dense enough, and are, therefore, sub-optimal for DNNs. We propose a hybrid approach that employs dense block floating-point (BFP) arithmetic on dot product computations and FP arithmetic elsewhere. While using BFP improves the performance of dot product operations, that compose most of DNN computations, allowing values to freely float between dot product operations leads to a better choice of tensor exponents when converting values to back BFP. We show that models trained with hybrid BFP-FP arithmetic either match or outperform their FP32 counterparts, leading to more compact models and denser arithmetic in computing platforms.