Designing, Implementing, and Interfacing BFloat16 Arithmetic Processing Unit to RISC-V Pipelined Processor

Abstract

In this paper, we present the implementation of Brain floating point format (BFloat16) floating point arithmetic unit in the execute pipeline of the RISC-V processor. For a certain class of applications like deep neural networks, this format significantly improves the power, performance, and area (PPA) metrics of the processor as compared to a single precision format. To validate our approach, we developed a dedicated BFloat16 floating point arithmetic unit and conducted a comprehensive comparison with the conventional single precision floating point unit (FPU32). The BFloat16 format demonstrates a well-balanced trade-off between computational advantages and storage benefits when compared to the IEEE-754 single precision format. The proposed unit extends the capabilities of the RISC-V processor to efficiently handle BFloat16 computations, incorporates architectural modifications and instruction set extensions; achieving enhanced performance. Implementation on an Artix-7 FPGA (XC7a35tcpg236-1) allowed us to assess resource utilization and timing delay. Additionally, the arithmetic units of both formats are implemented on ASIC using gf180 PDK (process design kit) using OpenROAD toolchain. The results show that the BFloat16 unit consumes fewer resources and also computes faster than the existing FPU32 operations. The proposed BFloat16-compliant RISC-V core can run at a maximum frequency of 47 MHz with a power consumption of 0.075W. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.