ONNX: Open Neural Network Exchange

• ONNX is an open, framework-agnostic standard that represents AI models as a unified graph of computation operators enabling seamless model exchange.
• It facilitates interoperability and optimization by allowing models to be exported from one framework and deployed on various hardware backends with minimal accuracy loss.
• ONNX supports advanced features like quantization, hardware integration, and formal verification, making it essential for robust AI model deployment.

The Open Neural Network Exchange (ONNX) is an open, framework-agnostic standard designed to represent artificial intelligence models as a unified graph of computation operators. ONNX serves as a cross-platform intermediate representation facilitating interoperability, portability, and optimization of neural network models across a rapidly evolving ecosystem of machine learning frameworks, deployment backends, and specialized hardware. As such, ONNX has become critical for diverse applications spanning model deployment, compilation, verification, explainability, hardware acceleration, and model optimization.

1. Fundamental Structure and Interoperability

ONNX defines a standardized set of computation operators and model graph schemas, enabling neural network models to be faithfully exported from one framework (e.g., PyTorch, TensorFlow, Caffe) and imported or deployed in another environment without requiring translation of framework-specific semantics. Each ONNX model comprises a directed acyclic graph (DAG) of operators, with well-specified attributes and tensor types, facilitating platform-independent representation and manipulation (Cai et al., 2019, Jin et al., 2020, Rausch et al., 2021). This structure decouples model development from hardware or backend specifics, ensuring that models can move seamlessly from training environments to targets such as inference engines, hardware NPUs, or interactive theorem provers (Jin et al., 2020, Daggitt et al., 2022).

The interoperability provided by ONNX has been systematically evaluated: empirical research demonstrates that conversion to ONNX preserves prediction accuracy, reduces model size, and typically maintains (or improves) runtime characteristics such as inference latency and memory footprint. For instance, studies converting models from PyTorch and Keras to ONNX and deploying them in ONNX Runtime found negligible changes in prediction accuracy (absolute and relative error close to zero), reduced file sizes, and inference performance comparable or superior to original models (Openja et al., 2022). ONNX-converted models have also been shown to preserve adversarial robustness at levels equivalent to originals in most cases.

2. Conformance, Testing, and Model Translation

Ensuring that ONNX operators behave identically across backends is critical for trustworthy deployment and model exchange. However, numerous challenges complicate this effort: operators have diverse constraints (e.g., Asin accepts only [–1,1], Split enforces axis bounds), and reference implementations for complex operators such as LSTM are labor-intensive and error-prone to maintain (Cai et al., 2019). Insufficient coverage in conformance tests can allow silent specification drift, resulting in semantically incorrect or inconsistent models after translation.

Sionnx is an automated unit test generation system for ONNX conformance that introduces a formal Operator Specification Language (OSL) and a three-phase randomization algorithm (TDBc-gen) to systematically produce comprehensive, specification-driven test cases for ONNX operators (Cai et al., 2019). OSL captures attribute constraints, operand properties, and inter-parameter dependencies in a compact DSL (TableGen-based), enabling fine-grained, automated test specification. Sionnx supports cross-framework verification by running generated tests across different ONNX runtimes and comparing output with reference implementations (NumPy or TensorFlow), detecting deviations and ensuring backend compliance.

The process of model translation between frameworks is nontrivial: failure analysis of core ONNX converters (torch.onnx, tf2onnx) reveals that approximately 75% of all defects occur at the node conversion stage. A significant proportion of failures (~33%) manifest as semantically incorrect models—conversion completes and models load, but runtime behavior deviates from the source (Jajal et al., 2023). Importantly, the presence of unusual node types is not a sufficient predictor; specific operator sequences can trigger subtle bugs, suggesting a need for behavioral tolerances, differential testing, and deeper coverage metrics.

3. Compilation, Optimization, and Quantization

ONNX provides the essential intermediate representation (IR) for modern compilation and optimization pipelines. Compilers such as onnx-mlir leverage ONNX as the input dialect, translating models to MLIR (Multi-Level Intermediate Representation) and progressively lowering through custom dialects (e.g., ONNX-specific and loop-based/Krnl dialects). This enables sophisticated graph-level rewriting, operator fusion, constant propagation, and affine loop optimizations before generating native code for diverse hardware backends (Jin et al., 2020). Data-centric frameworks further lower ONNX models into intermediate representations (e.g., SDFG graphs), exposing explicit data movement for systematic minimization of memory traffic and kernel fusion (Rausch et al., 2021).

Quantization support in ONNX has progressed from fixed 8-bit paradigms to arbitrary-precision and mixed-precision quantization. Techniques such as integer clipping appended to standard quantization operators (producing QCDQ and quantized-operator-with-clipping formats), along with the introduction of higher-level QONNX operators (Quant, BipolarQuant, Trunc), enable precise representation of sub-8-bit and uniform quantization across models (Pappalardo et al., 2022). These formats allow expressive modeling of quantization intent and facilitate targeting CPUs, GPUs, ASICs, and FPGAs.

Selective quantization is now supported through profiling-enabled toolchains such as TuneQn (Louloudakis et al., 16 Jul 2025). Instead of uniformly quantizing all layers, TuneQn evaluates the sensitivity of each layer to quantization (using metrics such as QDQ error and XModel error, normalized and combined as $$0.5 \cdot \text{norm}_\text{xmodel\_err} + 0.5 \cdot \text{norm}_\text{qdq\_err}$$), quantizes insensitive layers, and uses Pareto-Front multi-objective optimization (model size vs. accuracy loss) to present optimal quantization candidates. Empirical results document up to 54.14% reduction in accuracy loss and 72.9% smaller models compared to fully quantized or original models.

4. Hardware Integration and Adaptive Inference

ONNX serves as the bridge between high-level model design and hardware-optimized deployment. Toolchains parsing ONNX (including QONNX) automatically extract architectural parameters and generate streaming dataflow descriptions for FPGAs, producing accelerator hardware with modular, template-based architectures (Manca et al., 2023, Manca et al., 13 Jun 2024). These pipelines support adaptivity: by merging multiple non-adaptive profiles (differing in precision and configuration) and runtime management, the resulting accelerator can switch configurations dynamically according to power, latency, or workload requirements (e.g., using a Profile Manager). Approximate computing strategies—such as mixed-precision arithmetic and actor-level adaptation—permit real-time trade-offs between accuracy and energy efficiency at the edge.

Comparative evaluations confirm that ONNX-based flows deliver competitive or superior performance, and uniquely offer runtime adaptivity on FPGAs—a feature unavailable in frameworks that yield only static accelerators. Detailed integration with high-level synthesis tools (e.g., Vivado HLS, Vitis HLS, MDC) automates mapping from ONNX operators (e.g., convolutional layers: $Y(i, j) = \sum_{m,n} X(i+m, j+n)\cdot W(m,n)$) to parameterized hardware blocks.

For system simulation and architecture exploration, ONNX is directly supported in the ONNXim multi-core NPU simulator (Ham et al., 12 Jun 2024). ONNXim enables rapid cycle-level simulation of DNN inference under diverse multi-tenant and multi-model configurations, efficiently leveraging the deterministic nature of on-chip compute and modeling DRAM and NoC contention at cycle granularity.

5. Verification, Explainability, and Model Pruning

The adoption of ONNX as the canonical model description has enabled formal verification and explainability ecosystems. The Vehicle framework uses ONNX as the single, authoritative representation of the neural network, bridging the interaction between SMT-based verifiers (e.g., Marabou) and interactive theorem provers (e.g., Agda) (Daggitt et al., 2022). High-level assertions about neural network behavior (e.g., safety under bounded disturbances) are declared in a specialized DSL, type-checked, and transformed into both low-level queries and ITP code, enabling scalable, maintainable verification for large models (>20,000 nodes). The external ONNX file, tracked by hash, ensures alignment between verified and deployed models and supports network retraining without breaking proofs.

For explainability, ONNXExplainer introduces an ONNX-native, deployable scheme for computing Shapley values via automatic differentiation and DeepLIFT-inspired multiplier propagation (Zhao et al., 2023). It constructs forward and backward graphs, computes operator-specific attributions, and caches intermediate results, enabling efficient, one-shot computation of feature attributions compatible with production inference servers. Latency improvements of up to 500% versus standard SHAP implementations have been demonstrated.

Pruning at scale is supported by ONNXPruner, which unifies pruning workflows using node association trees to capture dependencies between pruned nodes and their associated subgraphs (Ren et al., 10 Apr 2024). This “tree-level evaluation” enables joint importance assessment across connected nodes, fostering more accurate filter selection. ONNXPruner’s experiments evidence improved accuracy retention relative to traditional single-node criteria, even on complex architectures (e.g., ResNet50, ViT), without requiring manual intervention or architecture adaptations.

6. Quality Assurance, Optimization Correctness, and Limitations

Despite ONNX’s standardization benefits, defects in model converters and optimizer passes persist, highlighting the need for systematic validation. Differential testing tools such as OODTE run the original and optimized ONNX models (both from local sources and the Model Hub) over standard datasets, compare outputs using domain-appropriate metrics (e.g., top-K accuracy, IoU for detection, BLEU for text), and isolate the responsible optimization pass when discrepancies are found. Recent evaluation shows that 9.2% of models cause optimizer crashes or invalid models, and substantial fractions exhibit output deviations or accuracy loss in specific domains (30% in classification, 16.6% in detection/segmentation) after optimization (Louloudakis et al., 3 May 2025). Issues such as improper graph upgrade, unsafe node fusion, or format versioning have been documented and reported. This motivates the integration of comprehensive regression and differential testing, robust validation beyond syntactic checks, and the refinement of tolerant conversion and optimization methodologies.

7. Future Directions and Ecosystem Impact

ONNX continues to function as the lingua franca for model exchange, optimization, and deployment. Future enhancements involve tighter integrations with hardware synthesis toolchains (expanding support for new accelerators and FPGAs), expanded operator coverage and accuracy in conversion tools, and advanced methodologies for explainability, pruning, and verification. The ONNX Model Hub and affiliated tooling (e.g., Explainability, Quantization, Pruning suites) now facilitate model curation, benchmarking, and cross-domain comparability.

As both the ONNX specification and its tool ecosystem evolve, research increasingly focuses on network correctness under optimization, automated test generation, and broader support for advanced quantization and hardware adaptivity. ONNX’s role as not merely a passive exchange format but as an active enabler for reproducible, efficient, verified, and transparent deployment is expected to deepen across machine learning research and industrial domains.