Bucket Getter: A Bucket-based Processing Engine for Low-bit Block Floating Point (BFP) DNNs

Block floating point (BFP), an efficient numerical system for deep neural networks (DNNs), achieves a good trade-off between dynamic range and hardware costs. Specifically, prior works have demonstrated that BFP format with 3 ∼ 5-bit mantissa can achieve FP32-comparable accuracy for various DNN workloads. We find that the floating-point adder (FP-Acc), which contains modules for normalization, alignment, addition, and fixed-point-to-floating-point (FXP2FP) conversion, dominates the power and area overheads, hence hindering the hardware efficiency of state-of-the-art low-bit BFP processing engines (BFP-PE).