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Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis

Kiran Thorat, Jiahui Zhao, Yaotian Liu, Hongwu Peng, Xi Xie, Bin Lei, Jeff Zhang, Caiwen Ding

TL;DR

The paper tackles the challenge of generating synthesizable Verilog code with explicit Power-Performance-Area (PPA) constraints by introducing VeriPPA, an open-source framework that combines two-stage refinement, error-informed prompts, and PPA-aware synthesis. It integrates an iterative loop of code generation, syntax/functional verification via the iverilog simulator, and PPA checks using Synopsys Design Compiler on the ASAP 7nm PDK, with feedback-driven refinement (VeriRectify) and multi-round error correction. A key contribution is the integration of in-context learning and detailed simulator diagnostics to significantly boost both syntactic correctness and functional accuracy compared to prior benchmarks, achieving up to 81.37% syntax and 62.0% functionality, while also delivering practical PPA improvements (e.g., substantial clock-constraint optimizations). These results demonstrate that LLMs, when guided by precise error feedback and PPA-aware prompts, can robustly tackle complex hardware design tasks, potentially accelerating hardware prototyping and verification workflows in ASIC/FPGA contexts.

Abstract

The increasing use of Advanced Language Models (ALMs) in diverse sectors, particularly due to their impressive capability to generate top-tier content following linguistic instructions, forms the core of this investigation. This study probes into ALMs' deployment in electronic hardware design, with a specific emphasis on the synthesis and enhancement of Verilog programming. We introduce an innovative framework, crafted to assess and amplify ALMs' productivity in this niche. The methodology commences with the initial crafting of Verilog programming via ALMs, succeeded by a distinct dual-stage refinement protocol. The premier stage prioritizes augmenting the code's operational and linguistic precision, while the latter stage is dedicated to aligning the code with Power-Performance-Area (PPA) benchmarks, a pivotal component in proficient hardware design. This bifurcated strategy, merging error remediation with PPA enhancement, has yielded substantial upgrades in the caliber of ALM-created Verilog programming. Our framework achieves an 81.37% rate in linguistic accuracy and 62.0% in operational efficacy in programming synthesis, surpassing current leading-edge techniques, such as 73% in linguistic accuracy and 46% in operational efficacy. These findings illuminate ALMs' aptitude in tackling complex technical domains and signal a positive shift in the mechanization of hardware design operations.

Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis

TL;DR

The paper tackles the challenge of generating synthesizable Verilog code with explicit Power-Performance-Area (PPA) constraints by introducing VeriPPA, an open-source framework that combines two-stage refinement, error-informed prompts, and PPA-aware synthesis. It integrates an iterative loop of code generation, syntax/functional verification via the iverilog simulator, and PPA checks using Synopsys Design Compiler on the ASAP 7nm PDK, with feedback-driven refinement (VeriRectify) and multi-round error correction. A key contribution is the integration of in-context learning and detailed simulator diagnostics to significantly boost both syntactic correctness and functional accuracy compared to prior benchmarks, achieving up to 81.37% syntax and 62.0% functionality, while also delivering practical PPA improvements (e.g., substantial clock-constraint optimizations). These results demonstrate that LLMs, when guided by precise error feedback and PPA-aware prompts, can robustly tackle complex hardware design tasks, potentially accelerating hardware prototyping and verification workflows in ASIC/FPGA contexts.

Abstract

The increasing use of Advanced Language Models (ALMs) in diverse sectors, particularly due to their impressive capability to generate top-tier content following linguistic instructions, forms the core of this investigation. This study probes into ALMs' deployment in electronic hardware design, with a specific emphasis on the synthesis and enhancement of Verilog programming. We introduce an innovative framework, crafted to assess and amplify ALMs' productivity in this niche. The methodology commences with the initial crafting of Verilog programming via ALMs, succeeded by a distinct dual-stage refinement protocol. The premier stage prioritizes augmenting the code's operational and linguistic precision, while the latter stage is dedicated to aligning the code with Power-Performance-Area (PPA) benchmarks, a pivotal component in proficient hardware design. This bifurcated strategy, merging error remediation with PPA enhancement, has yielded substantial upgrades in the caliber of ALM-created Verilog programming. Our framework achieves an 81.37% rate in linguistic accuracy and 62.0% in operational efficacy in programming synthesis, surpassing current leading-edge techniques, such as 73% in linguistic accuracy and 46% in operational efficacy. These findings illuminate ALMs' aptitude in tackling complex technical domains and signal a positive shift in the mechanization of hardware design operations.
Paper Structure (15 sections, 4 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 15 sections, 4 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. VeriPPA FRAMEWORK
  4. Design Overview
  5. Code Generation and Testing
  6. VeriRectify
  7. Multi-round Conversation with Error Feedback
  8. Power Performance & Area (PPA) Checking
  9. In-Context Learning
  10. Evaluation
  11. Datasets
  12. Experimental Setup
  13. Generation Correctness
  14. PPA Optimization
  15. conlcusion

Figures (5)

  • Figure 1: This visualization captures the step-by-step process where an LLM synthesizes Verilog codes from hardware design prompts, with the ensuing code subjected to thorough validation by a Simulator and scrutinized for adherence to Power-Performance-Area (PPA) checks.
  • Figure 2: The diagram illustrates the process of syntactic and functional code verification.
  • Figure 3: Correctness of generated Verilog code with respect to correction attempt on RTLLM, using (a) GPT-3.5; (b) GPT-4-v1; (c) GPT-4; (d) GPT-4-4shot
  • Figure 4: Correctness of generated Verilog code with respect to correction attempt on VerilogEval, using (a) VerilogEval-Machine, and (b) VerilogEval-Human.
  • Figure 5: Optimization Flow.