TD-Interpreter: AI VQA for Timing Diagrams

• TD-Interpreter is an AI-powered tool that specializes in interpreting and analyzing complex timing diagrams in digital design.
• It fuses a Vision Transformer with a fine-tuned language model using LoRA adaptation, enabling precise multimodal reasoning.
• Its synthetic data generation pipeline and chain-of-thought prompting yield expert-level analysis that outperforms general-purpose models.

TD-Interpreter is an AI-powered visual–language tool engineered to assist engineers in the comprehension, verification, and analysis of complex timing diagrams (TDs), which are commonly encountered in digital design and hardware verification contexts. The system provides a multimodal Visual Question–Answering (VQA) framework, optimized for the particular patterns of timing diagrams by integrating a fine-tuned vision–LLM with a synthetic data generation and alignment workflow. TD-Interpreter achieves expert-level, context-sensitive reasoning and explanation, outperforming general-purpose large multimodal models such as GPT-4o across a wide range of benchmarked design and verification tasks (He et al., 20 Jul 2025).

1. Visual-Language Question–Answer Environment and Model Architecture

At the core, TD-Interpreter provides a VQA environment where users supply a timing diagram image ($X_v \in \mathbb{R}^{H \times W \times 3}$) and a natural language query ($X_q$). The goal is to synthesize an answer $X_a$ that is contextually tailored and technically accurate.

The architecture comprises:

• A vision encoder $g(\cdot)$ based on a Vision Transformer (ViT), which embeds the pixel space into visual features $h_v = g(X_v)$.
• A transformer-based LLM $f(\cdot)$, derived from LLaMA-family checkpoints, which fuses $h_v$ with the token embeddings of the question and decodes an answer autoregressively:

$$p(X_a | X_v, X_q) = \prod_{i=1}^{l} p(x_i | X_v, X_q, X_{a,<i})$$

This fusion enables the model to interpret both spatial (e.g., waveform alignment) and temporal (e.g., clock edge sequencing) details in the diagram, integrating them with the semantics of the posed question.

Common supported queries include point-wise signal inspection (e.g., "What is the value of signal s at cycle n?"), high-level summary ("Summarize the sequence of events"), protocol verification, and FSM extraction.

2. Synthetic Data Generation and Grounded Supervision

The absence of a standard, fully annotated dataset of timing diagrams required the design of a complete synthetic data generation pipeline:

• For concrete diagrams (from RTL designs), Verilog modules are parsed and port information extracted, then simulated via waveform generation tools (using Iverilog to produce .vcd files). These are transformed to JSON representations with vcd2json, and rendered as diagram images with wavedrom-cli.
• For abstract/protocol diagrams (e.g., AMBA bus specifications), corresponding JSON is prepared manually and further randomized (shuffling signal order, adjusting clock cycles, etc.) to diversify the domain.
• QA pair generation operates in two styles:
  • Caption-based QA: Model is prompted to produce descriptive or instructive captions, e.g., "Describe what happens at each clock edge."
  • Reasoning-based QA: Complex queries (e.g., "What causes a state transition here?") are paired with detailed, step-by-step answers.
• Chain-of-thought prompting (using models such as DeepSeek-Coder-V2) is employed to yield intermediate logical or structural reasoning steps for long-form diagram analysis.

This synthetic workflow ensures high alignment between visual waveform features and their textual interpretation, supporting effective supervised training.

3. Training Regime and Parameter-Efficient Adaptation

TD-Interpreter implements parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation). Only the LLM weights are adapted, while the vision encoder remains frozen.

• Given the pretrained weight matrix $W_0 \in \mathbb{R}^{d \times k}$, an update is parameterized as $W_0 + \Delta W$, where $\Delta W = BA$ ($$A \in \mathbb{R}^{r \times k}, B \in \mathbb{R}^{d \times r}, r \ll d, k$$).
• Training proceeds with approximately $10^4$ carefully selected QA examples (from a pool of millions), on 8 NVIDIA GPUs for roughly 17 hours, with LoRA rank $r=8$.
• Only the lightweight adaptation heads are updated, minimizing overfitting and computational footprint even as the model generalizes over a vast space of timing diagrams.

The ViT encoder processes the input image by dividing it into patches ($x_v \in \mathbb{R}^{N \times P^2 \cdot C}$, with $N = (H \times W)/P^2$), which are embedded and passed through $n_v$ transformer blocks. The token and vision features are concatenated and processed jointly through $n_t$ language transformer blocks.

4. Benchmarking and Comparative Performance

TD-Interpreter was evaluated against GPT-4o and domain experts on a suite of benchmarks, including realistic and synthetic TDs spanning:

• AHB burst transfers and protocol event detection
• FSM synthesis for timers and counters
• Asynchronous FIFO signal integrity checks
• SPI protocol phase/arbitration analysis

Results demonstrate:

• Text-retrieval metrics (BLEU-4, ROUGE) above 95, indicating close alignment with expert-generated captions and explanations.
• Reasoning accuracy, with TD-Interpreter accurately synthesizing and explaining state transitions, clock-domain crossings, and edge-triggered events with referential precision.
• Outperformance of baseline models: GPT-4o, while strong in general visual-linguistic QA, often produces generic explanations, misses domain-specific waveform nuances, or misidentifies signals (e.g., in cases involving AHB wait states or specific SPI transitions).
• Expert-like stepwise reasoning: In tasks such as FSM inference, TD-Interpreter provides precise, clock-indexed state transition mapping (e.g., identifying the exact clock edge responsible for flip-flop triggering or protocol handshake).

5. Architectural and Methodological Contributions

Several methodological advances underpin the accuracy and utility of TD-Interpreter:

• Vision-LLM fusion: The explicit design enables simultaneous parsing of multi-channel, multi-cycle diagrams and their corresponding textual semantics.
• Synthetic data and alignment: The data generation pipeline ensures each diagram is "grounded" by its simulation history or formal specification, supporting faithful question–answer pairings.
• Parameter-efficient adaptation: LoRA enables the rapid specialization of large models with limited training data, relevant in domains where annotating real data is costly.
• Chain-of-thought prompting: The generation pipeline produces instructive intermediate reasoning chains, enabling both longer explanations and improved model faithfulness.

6. Practical Implications and Expert Use

TD-Interpreter serves three main practical functions:

• Accelerating design comprehension: Engineers can quickly interpret diagrams not of their own authorship, receiving precise answers and design logic without manual tracing.
• Verification and debugging: The deep, context-aware reasoning outperforms generic QA by pinpointing root causes of protocol failures and timing misalignments (e.g., identifying hazardous clock crossings).
• Efficient deployment: Fast inference and high-fidelity answers make it suitable for integration into EDA (Electronic Design Automation) toolchains and large-scale enterprise workflows.

7. Limitations and Future Research

While TD-Interpreter establishes new state-of-the-art performance for timing diagram comprehension, some limitations and directions for continued improvement are apparent:

• Dataset coverage: Synthetic data may not fully capture all edge cases or proprietary vendor-specific diagram conventions.
• Complex multi-modal queries: Advanced forms of multi-step combinatorial analysis (e.g., multi-path verification over parametrized diagrams) may still challenge current architectures.
• Distribution of adaptation: Expanding LoRA adaptation or domain-specific prompting to new types of waveforms or analog-mixed-signal diagrams may require additional research.

A plausible implication is that broader adoption of similar domain-specific multimodal architectures could systematically improve machine understanding in hardware, protocol, and electronic systems engineering contexts.

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In summary, TD-Interpreter (He et al., 20 Jul 2025) is a domain-specialized multimodal VQA system for timing diagrams, integrating structured synthetic data generation, advanced model adaptation techniques, and task-aligned reasoning to deliver expert-level interpretability and assistance in digital design and verification processes. Its technical and methodological choices position it as a leading solution for automated timing diagram analysis in engineering workflows.