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Rivotek AI Application Development Guide
Developing AI applications for Rivotek platforms requires understanding the AI development workflow. This guide explains the complete development process from model selection to deployment.
Development Workflow
1. Model Selection
Choose pre-trained models or train custom models using frameworks like TensorFlow or PyTorch.2. Model Optimization
Convert and optimize models for NPU deployment:- Quantization: Convert FP32 to INT8
- Pruning: Remove redundant connections
- Fusion: Merge layers for efficiency
3. Deployment
Deploy optimized models on target hardware using Rivotek AI SDK.4. Testing
Validate performance and accuracy on target hardware.Best Practices
- Start with reference models
- Benchmark early and often
- Optimize for target NPU
- Test in real conditions
💡 FAE Insights
Professional Insight
Successful AI deployment requires optimization for target hardware.
⚠️ Common Pitfalls
- ✗ Deploying unoptimized models
- ✗ Not benchmarking on target
- ✗ Ignoring quantization effects
- ✗ Over-optimizing and losing accuracy
📋 Customer Cases
Smart Retail Company
Retail
Challenge
AI model too slow for real-time analysis
Solution
Optimized model using quantization and pruning
Results
Achieved 3x speedup with 99% accuracy retention