Branch Prediction in Hardcaml for a RISC-V 32im CPU

Abstract

This paper presents a hardware implementation of aggressive branch prediction techniques for a high-performance RISC-V 32im CPU core. The methodology progresses from simple static decode stage predictions to the utilization of the advanced BATAGE predictor design. Crucially, the work details the author's experience writing the Register Transfer Level (RTL) using Hardcaml, a specialized hardware description library based on the OCaml functional programming language.

Report

Structured Report: Branch Prediction in Hardcaml for a RISC-V 32im CPU

Key Highlights

• High-Performance Focus: The core objective is enhancing instruction stream processing performance through accurate branch prediction.
• Predictor Evolution: The implementation spans multiple prediction techniques, beginning with basic static decode predictions and culminating in an advanced design.
• BATAGE Implementation: The final and most sophisticated branch predictor utilized in the design is BATAGE.
• Hardcaml Methodology: The entire RTL implementation was executed using Hardcaml, demonstrating the viability of functional programming (OCaml) for complex hardware description.

Technical Details

• Target CPU: The implementation targets a RV32IM processor, corresponding to the 32-bit RISC-V instruction set with standard Integer and Multiplication extensions.
• RTL Language: The hardware description was written entirely in Hardcaml, a DSL embedded within the OCaml functional language.
• Prediction Stages: The prediction mechanism involves at least two distinct levels: initial static decode stage predictions and the final BATAGE mechanism (a complex dynamic predictor, often a variant of the TAGE predictor).
• Domain: The paper falls under the discipline of Hardware Architecture (cs.AR).

Implications

• RISC-V Performance Boost: The successful integration of an advanced predictor like BATAGE into a RV32IM core demonstrates a viable path for achieving high-end speculative performance necessary for deeper pipelines in the RISC-V ecosystem.
• Validation of Functional Hardware Design: Utilizing Hardcaml validates the use of modern functional programming techniques for generating RTL. This approach often leads to more robust, easily verifiable, and modular hardware designs compared to traditional hardware description languages (HDLs).
• Open Source Development: The project contributes a practical example of implementing complex micro-architectural features using Hardcaml, potentially lowering the barrier to entry for developing advanced RISC-V cores outside of conventional commercial toolchains.
• Future Architectural Benchmarking: The detailed experience report on Hardcaml provides valuable data for researchers and developers considering alternative hardware description flows for future high-performance computing projects.