PPU: Design and Implementation of a Pipelined Full Posit Processing Unit

TL;DR

This paper introduces a pipelined full posit processing unit integrated into a RISC-V core, enhancing numerical accuracy and range with minimal area increase, and demonstrates its effectiveness in neural network applications.

Contribution

It presents the design, implementation, and FPGA prototyping of a full posit processing unit for RISC-V, enabling hardware support for posit arithmetic operations.

Findings

The FPPU increases core area by 7-15% depending on posit size.

It achieves efficient hardware implementation of all basic posit operations.

Using 16-bit posits results in minimal accuracy loss in neural networks.

Abstract

By exploiting the modular RISC-V ISA this paper presents the customization of instruction set with posit\textsuperscript{\texttrademark} arithmetic instructions to provide improved numerical accuracy, well-defined behavior and increased range of representable numbers while keeping the flexibility and benefits of open-source ISA, like no licensing and royalty fee and community development. In this work we present the design, implementation and integration into the low-power Ibex RISC-V core of a full posit processing unit capable to directly implement in hardware the four arithmetic operations (add, sub, mul, div and fma), the inversion, the float-to-posit and posit-to-float conversions. We evaluate speed, power and area of this unit (that we have called Full Posit Processing Unit). The FPPU has been prototyped on Alveo and Kintex FPGAs, and its impact on the metrics of the full-RISC-V core have been evaluated, showing that we can provide real number processing capabilities to the mentioned core with an increase in area limited to $7\%$ for 8-bit posits and to $15\%$ for 16-bit posits. Finally we present tests one the use of posits for deep neural networks with different network models and datasets, showing minimal drop in accuracy when using 16-bit posits instead of 32-bit IEEE floats.