Title: Training DNNs with Hybrid Block Floating Point,
Abstract: The wide adoption of DNNs has given birth to unrelenting computing
requirements, forcing datacenter operators to adopt domain-specific
accelerators to train them. These accelerators typically employ densely packed
full precision floating-point arithmetic to maximize performance per area.
Ongoing research efforts seek to further increase that performance density by
replacing floating-point with fixed-point arithmetic. However, a significant
roadblock for these attempts has been fixed point's narrow dynamic range, which
is insufficient for DNN training convergence. We identify block floating point
(BFP) as a promising alternative representation since it exhibits wide dynamic
range and enables the majority of DNN operations to be performed with
fixed-point logic. Unfortunately, BFP alone introduces several limitations that
preclude its direct applicability. In this work, we introduce HBFP, a hybrid
BFP-FP approach, which performs all dot products in BFP and other operations in
floating point. HBFP delivers the best of both worlds: the high accuracy of
floating point at the superior hardware density of fixed point. For a wide
variety of models, we show that HBFP matches floating point's accuracy while
enabling hardware implementations that deliver up to 8.5x higher throughput.