Programming Language Assisted Waveform Analysis: A Case Study on the Instruction Performance of SERV

Abstract

This paper addresses the difficulty of fine-grained RISC-V core analysis in a rapidly expanding ecosystem by proposing a programming language-assisted methodology. The innovation utilizes WAWK, a front-end for the Waveform Analysis Language, to automate detailed performance metrics extraction directly from simulation waveforms. The case study successfully applies this method to calculate the instruction performance of SERV, a bit-serial RISC-V core, demonstrating high efficiency requiring only minimal lines of code.

Report

Key Highlights

• The study addresses the challenge of manually analyzing the performance characteristics of the continuously growing number of available RISC-V cores.
• It introduces a methodology leveraging powerful programming languages to achieve the necessary productivity for analyzing hardware developments.
• The core tool used is WAWK, which acts as a front-end for the open-source Waveform Analysis Language.
• The technique enables fine-grained analysis, calculating performance metrics for every instruction of a core, which is superior to generalized software benchmarks.
• The approach was validated through a case study analyzing the instruction performance of SERV, a well-known bit-serial RISC-V core, demonstrating that the calculation requires only a few lines of code.

Technical Details

• Methodology: Programming Language Assisted Waveform Analysis.
• Tool Architecture: WAWK (Waveform Analysis Language front-end).
• Data Source: Generated simulation waveforms (VCD files or similar representations of core activity over time).
• Target Application: Measuring the performance (cycle count/latency) of all individual instructions of a RISC-V core.
• Case Study Core: SERV, a bit-serial implementation of the RISC-V architecture.
• Efficiency: The method claims to significantly reduce the implementation complexity, needing only "a few lines of code" for metric calculation.

Implications

• Ecosystem Scalability: This automated analysis method is crucial for managing the diverse and fast-paced growth of the RISC-V ecosystem, providing a scalable alternative to manual verification.
• Design Decision Support: Providing fine-grained, instruction-level performance data helps engineers select the most appropriate core for specific project requirements, ensuring a successful hardware implementation.
• Productivity Boost: By automating complex analysis tasks using robust programming tools, hardware engineers can achieve higher productivity, matching the rapid development speed characteristic of the RISC-V community.
• Quality Assurance: The technique facilitates detailed performance profiling and optimization, leading to higher quality and better-optimized RISC-V core implementations.