Efficient AI Model Deployment Using Quantization Analysis Tool

I. INTRODUCTION

• Deep neural networks are growing in size and computational intensity, challenging deployment on platforms with limited compute, memory bandwidth, and power.The paper names embedded systems, mobile devices, and edge accelerators as examples.Source: §I · p. 1
• Quantization reduces model size, memory footprint, and power use by lowering parameter precision, but its errors can reduce accuracy.Choosing a scheme that balances accuracy, size, and performance remains challenging.Source: §I · p. 1
• The Quantization Analysis Tool uses ONNX to analyze quantization effects layer by layer, visualize weight and activation distributions, and guide precision assignment.Its analysis is intended to help identify problematic operations before deployment.Source: §I · p. 1

II. BACKGROUND

• When quantization accuracy drops, higher bit-widths can reduce quantization error, but applying 16/32-bit precision across the model increases compute and memory requirements.The paper describes selective or adaptive bit-width approaches and introduces a tool that compares float and fully quantized models to identify problematic operations.Source: §II · p. 2

A. Model Quantization

• Quantization lowers parameter precision, such as from 32-bit floating point to 8-bit integers, to reduce memory footprint and speed inference, but can lower accuracy.The paper contrasts PTQ, which simplifies deployment but may hurt sensitive layers, with QAT, which offers higher accuracy but is computationally expensive and harder to integrate.Source: A. Model Quantization · p. 2
• Different layers have different quantization sensitivities, so uniform quantization can produce sub-optimal performance.The paper identifies layer-wise analysis as important for understanding this variation.Source: A. Model Quantization · p. 2
• Selective quantization can preserve accuracy by quantizing only some layers or assigning them different bit widths, but its strategy is generally ad hoc and difficult to balance against efficiency.It is also useful when target hardware cannot support particular lower precisions, making full-model quantization infeasible.Source: A. Model Quantization · p. 2

B. ONNX Framework

• ONNX transfers models across training frameworks and deployment runtimes, while its graph format and quantization operators support inspection and experimentation with quantization schemes.ONNX Runtime APIs and hardware-accelerated backends support flexible evaluation of quantization effects.Source: B. ONNX Framework · p. 2

III. METHODOLOGY

• The tool combines user interaction, ONNX model parsing, statistical quantization analysis, and visual reporting in a workflow from model ingestion to quantitative assessment.The methodology introduces the tool's modules as parts of this workflow.Source: §III · p. 2

A. User Interface

• The browser interface accepts ONNX-compliant models and summarizes operator types, parameter counts, tensor shapes, and estimated FLOPs before users initiate quantization.Users select the quantization configuration through the interface.Source: A. User Interface · p. 2

B. ONNX Model Parsing

• The ONNX parser enumerates graph nodes, operator attributes, and dataflow dependencies, extracts learned parameters, infers activation shapes, and computes parameter counts.It estimates FLOPs using operator-specific formulas and uses the extracted information to generate the model summary shown in Fig. 1.Source: B. ONNX Model Parsing · pp. 2–3, Figure 1 · p. 3

C. Model Quantization Configuration

• Representative ImageNet batches are used to record activation statistics, which are converted into quantization scales and zero-points.Parameters can be computed per-tensor or per-channel, depending on the selected configuration.Source: C. Model Quantization Configuration · p. 3
• Users select data types, calibration and validation datasets, and a calibration method before generating quantization analysis results.The configuration is shown in Fig. 2.Source: C. Model Quantization Configuration · p. 3, Figure 2 · p. 3

D. Layer-wise sensitivity analysis

• Each layer is quantized individually while the rest remain FP32, and its accuracy drop or output deviation is measured against the FP32 baseline.The model is evaluated on the calibration dataset.Source: D. Layer-wise sensitivity analysis · p. 3
• High-sensitivity layers are flagged as unsuitable for aggressive quantization, while robust layers are recommended for lower precision.The resulting rankings guide selective mixed-precision strategies.Source: D. Layer-wise sensitivity analysis · p. 3
• Fig. 3 presents a layer-wise sensitivity analysis visualization.Operations are ordered topologically and classified as low, medium, or high sensitivity.Source: D. Layer-wise sensitivity analysis · p. 3, Figure 3 · p. 4

E. Distribution Analysis of Weights and Activations

• Weight distributions compare FP32 parameters with quantized counterparts, while activation distributions are collected during calibration to reflect runtime data flow.Fig. 4 shows these distributions for a ResNet model.Source: E. Distribution Analysis of Weights… · p. 4, Figure 4 · p. 4
• Overlaid histograms help identify clipping-prone outliers, narrow dynamic ranges, differences between symmetric and asymmetric quantization, and layer-specific skew.These distribution properties help explain sensitivity and support targeted refinement.Source: E. Distribution Analysis of Weights… · p. 4
• The tool provides actionable insights into quantization’s effects on model operations and overall accuracy.The paper connects these insights to quantization-friendly architecture design and efficient edge deployment.Source: E. Distribution Analysis of Weights… · p. 4

• Experiments cover MobileNet, ResNet, EfficientNet, SqueezeNet, and ShuffleNet, using a standard calibration dataset such as an ImageNet subset.Source: §IV · p. 4
• Tool-integrated diagnostics significantly improve quantized accuracy, with greater challenges reported for EfficientNet and SqueezeNet than for well-studied ResNet models.The passage attributes improvements to visual, statistical, and layer-wise analysis; Table I is cited in the prose, but its caption does not provide cell values.Source: §IV · p. 4, Table I · p. 4
• Fewer than 10 sensitive layers, representing less than about 5% of model operations, can be targeted to improve quantized accuracy.For EfficientNet, excluding Mul operations improves accuracy significantly, and the last Gemm operations are among the highly sensitive layers.Source: §IV · p. 4
• Sensitivity differs by architecture: early layers are sensitive in SqueezeNet and ShuffleNet, depthwise layers vary in MobileNet, and near-output fully connected layers are sensitive in EfficientNet.The passage links MobileNet depthwise sensitivity to activation-distribution shape and spread; it suggests skipping EfficientNet’s last fully connected stage may improve quantized accuracy.Source: §IV · pp. 4–5
• Skewed or heavy-tailed weight and activation distributions tend to mark layers needing higher precision, while compact near-Gaussian distributions are more stable.Weight errors are reported as significantly lower than activation errors, underscoring the importance of calibration data and method.Source: §IV · p. 5
• Layer-specific findings explain why uniform quantization can underperform mixed precision, while advanced clipping, automated mixed-precision search, and hardware-specific performance models remain improvement opportunities.Source: §IV · p. 5
• Node-wise quantization shows distinct accuracy patterns: ResNet size falls with little accuracy impact, MobileNet V2 drops sharply at its last layers, and ShuffleNet 1.1 drops in the middle.The analysis quantizes nodes in topological order and compares normalized accuracy and model size with the float model; MobileNet V2 also has a small initial-layer accuracy drop.Source: §IV · p. 5, Figure 6 · p. 5

• The Quantization Analysis Tool evaluates and optimizes ONNX quantized models through selective quantization, layer-wise sensitivity analysis, and statistical visualizations.Source: CONCLUSION · p. 5
• Tool-guided precision strategies outperform uniform post-training quantization, especially for complex models, and offer a structured approach to edge deployment.Source: CONCLUSION · p. 5
• Future work includes Transformer and LLM support, additional metric comparators, and evaluation on AI accelerators with limited INT8/INT16 or mixed-precision support.Source: CONCLUSION · p. 5

• The tool is available at https://github.com/dwithchenna/onnx-analyer.Source: AVAILABILITY · p. 5