# Question Decomposition

## Original Question
Should we switch from ONNX Runtime to native TensorRT Engine for all AI models in the GPS-Denied pipeline, running on Jetson Orin Nano Super?

## Active Mode
Mode B: Solution Assessment — existing solution_draft03.md uses ONNX Runtime / mixed inference. User requests focused investigation of TRT Engine migration.

## Question Type
Decision Support — evaluating a technology switch with cost/risk/benefit dimensions.

## Research Subject Boundary

| Dimension | Boundary |
|-----------|----------|
| Population | AI inference models in the GPS-Denied navigation pipeline |
| Hardware | Jetson Orin Nano Super (8GB LPDDR5, 67 TOPS sparse INT8, 1020 MHz GPU, NO DLA) |
| Software | JetPack 6.2 (CUDA 12.6, TensorRT 10.3, cuDNN 9.3) |
| Timeframe | Current (2025-2026), JetPack 6.2 era |

## AI Models in Pipeline

| Model | Type | Current Runtime | TRT Applicable? |
|-------|------|----------------|-----------------|
| cuVSLAM | Native CUDA library (closed-source) | CUDA native | NO — already CUDA-optimized binary |
| LiteSAM | PyTorch (MobileOne + TAIFormer + MinGRU) | Planned TRT FP16 | YES |
| XFeat | PyTorch (learned features) | XFeatTensorRT exists | YES |
| ESKF | Mathematical filter (Python/C++) | CPU/NumPy | NO — not an AI model |

Only LiteSAM and XFeat are convertible to TRT Engine. cuVSLAM is already NVIDIA-native CUDA.

## Decomposed Sub-Questions

1. What is the performance difference between ONNX Runtime and native TRT Engine on Jetson Orin Nano Super?
2. What is the memory overhead of ONNX Runtime vs native TRT on 8GB shared memory?
3. What conversion paths exist for PyTorch → TRT Engine on Jetson aarch64?
4. Are TRT engines hardware-specific? What's the deployment workflow?
5. What are the specific conversion steps for LiteSAM and XFeat?
6. Does Jetson Orin Nano Super have DLA for offloading?
7. What are the risks and limitations of going TRT-only?

## Timeliness Sensitivity Assessment

- **Research Topic**: TensorRT vs ONNX Runtime inference on Jetson Orin Nano Super
- **Sensitivity Level**: 🟠 High
- **Rationale**: TensorRT, JetPack, and ONNX Runtime release new versions frequently. Jetson Orin Nano Super mode is relatively new (JetPack 6.2, Jan 2025).
- **Source Time Window**: 12 months
- **Priority official sources to consult**:
  1. NVIDIA TensorRT documentation (docs.nvidia.com)
  2. NVIDIA JetPack 6.2 release notes
  3. ONNX Runtime GitHub issues (microsoft/onnxruntime)
  4. NVIDIA TensorRT GitHub issues (NVIDIA/TensorRT)
- **Key version information to verify**:
  - TensorRT: 10.3 (JetPack 6.2)
  - ONNX Runtime: 1.20.1+ (Jetson builds)
  - JetPack: 6.2
  - CUDA: 12.6
