Service Overview
Edge AI
Edge AI service deploying advanced models on automotive hardware (Qualcomm SA8255P SoC) for real-time driver emotion understanding and in-cabin personalization. Achieves sub-100ms continuous inference with 18.7× speed improvement through INT8 quantization and 116% throughput improvement — meeting strict automotive latency requirements without cloud dependency.
<100ms
inference latency
18.7×
speed improvement
5.99%
accuracy gain
49.15%
recall improvement
116%
throughput gain
Capabilities
Key capabilities
Sub-100ms Inference
Continuous real-time inference meeting strict automotive latency requirements on Qualcomm SA8255P SoC.
INT8 Quantization
Model optimization via INT8 quantization delivering 18.7× speed improvement and 116% throughput gain.
Driver Emotion AI
Fuses 20+ CAN signals to understand driver emotional state and context for in-cabin personalization.
Cloud-Independent Operation
All inference runs on-device — no cloud dependency, no latency penalty, no data transmission.
Automotive SoC Expertise
Deep integration with Qualcomm automotive platforms including native hardware acceleration.
Technology
Technology stack
| Component | Technology | Purpose |
|---|---|---|
| Hardware | Qualcomm Snapdragon, NVIDIA Orin | Edge computing platform |
| Framework | ROS2, TensorFlow Lite | AI model deployment |
| Sensors | LiDAR, Camera, Radar | Environmental perception |
| Communication | CAN-bus, Ethernet | Vehicle network integration |
| Development | C++, Python | Implementation language |
Use cases
Real-world applications
Documented outcomes from actual deployments.
Optimized AI Model for Edge Devices
Resolved QNN conversion pipeline degradation when converting AI models from PyTorch → ONNX → QNN for the Qualcomm SA8255P HTP backend. Root cause was a data layout mismatch during QNN conversion.
Before
QNN conversion produced degraded inference accuracy due to transpose processing mismatch
After
Functional equivalence fully restored on SA8255P HTP backend after graph modification
How we work
Implementation approach
Phase 1: Assessment & Planning
- Analyze current vehicle architecture and sensor configuration
- Identify AI use cases and performance requirements
- Define latency, accuracy, and reliability targets
Phase 2: Model Development & Training
- Develop custom AI models for specific use cases
- Train on automotive-specific datasets
- Optimize models for edge hardware constraints
Phase 3: Integration & Testing
- Integrate models with vehicle systems
- Conduct Hardware-in-the-Loop (HIL) testing
- Validate performance in real-world conditions
Phase 4: Deployment & Monitoring
- Deploy to vehicle fleet
- Monitor performance metrics and system health
- Iterate and improve based on real-world data
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