Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing

Abstract

This paper presents a novel, unified hardware solution for integrating lightweight and precision-scalable Posit arithmetic into RISC-V Floating Point Units (FPU), while maintaining compatibility with IEEE-754 standards. The implementation uses dedicated posit codecs, dynamic exponent sizing for scalability, and customized ISA extensions, addressing the high overhead typically associated with posit adoption. Comprehensive evaluation shows significant resource reduction (up to 57.4% fewer FFs) and notable performance gains, achieving up to 2.54x throughput improvement in crucial GEMM kernels.

Report

Key Highlights

• Unified Solution: Developed a unified hardware approach for implementing both Posit and IEEE-754 arithmetic within RISC-V cores.
• Lightweight Design: Achieved substantial hardware savings, demonstrating a 47.9% reduction in Look-Up Tables (LUTs) and a 57.4% reduction in Flip-Flops (FFs) compared to prior state-of-the-art posit-enabled RISC-V processors.
• Performance Improvement: Delivered a significant throughput enhancement, showing up to $2.54\times$ improvement in various General Matrix Multiplication (GEMM) kernels.
• Transprecision Goal: The solution aims specifically to enable efficient transprecision computing by leveraging the superior dynamic range and accuracy of the posit format.

Technical Details

• Architectural Enhancement: The approach involves enhancing existing RISC-V processors by integrating new components into the original FPU.
• Posit Implementation Method: Dedicated posit codecs were integrated into the FPU structure to ensure a lightweight implementation footprint.
• Scalability Method: Precision-scalability and multi/mixed-precision support are achieved via the incorporation of dynamic exponent sizing.
• Compatibility: The design reuses and customizes existing Instruction Set Architecture (ISA) extensions to ensure posit operations remain compatible with the established IEEE-754 framework.
• Evaluation Scope: The comprehensive evaluation covered the performance and resource utilization at three levels: the modified FPU, the full RISC-V core, and the System-on-Chip (SoC) level.

Implications

• Advancing Transprecision Computing: By offering a resource-efficient, scalable solution, this work accelerates the practical adoption of posit format, which is critical for demanding scientific simulations and AI/ML workloads requiring flexible numerical precision.
• Strengthening RISC-V: Provides a high-impact, standardized method for RISC-V designers to incorporate advanced numerical formats without massive hardware bloat, making RISC-V more competitive against architectures with established proprietary floating-point extensions.
• AI Acceleration Potential: The measured performance increase in GEMM (a foundational operation in deep learning) suggests that this implementation has high relevance for designing next-generation, high-throughput RISC-V accelerators for Artificial Intelligence.
• Low-Power/Edge Devices: The dramatic reduction in LUTs and FFs makes the integration of posit arithmetic viable for resource-constrained environments, such as edge computing devices and embedded systems.