ONE AI X Altera MAX® 10
Optimized AI architectures that make hardware secondary
The
whitepaper
from Altera and ONE WARE highlights a practical industrial use case: potato chip quality inspection.
The challenge: reliably detecting burn marks and defects in real time on a fast production line — under strict limits on latency, power, and cost.
Good
Defective (Burn Marks)
This example reflects a broader class of industrial AI challenges:
- Manufacturing: PCB and automotive part inspection, textile quality control
- Robotics: real-time perception and decision-making
- Agriculture: drone-based crop and soil monitoring
- Healthcare & mobility: compact diagnostic and sensing devices
AI Model Optimization as the Key Differentiator
The real breakthrough lies not in the hardware, but in the AI model design.
- Minimalistic Architecture: ONE AI generated a lean network with only 6,750 parameters and 0.0175 GOPs, compared to 127 million parameters and 25 GOPs for the conventional VGG19 baseline.
- Quantization-Aware Training (QAT): Training directly in INT8 preserves accuracy during quantization — a critical step for FPGA deployment.
- Smarter, not bigger: The optimized model reached 99.5% test accuracy, while the VGG19 reference managed only 88% on the same dataset.
This demonstrates how domain-specific, optimized architectures outperform oversized networks, avoiding overfitting and focusing only on the features that matter.
HDL Generation as an Efficiency Amplifier
Once optimized, the model is compiled into RTL/HDL and deployed on Altera’s MAX® 10 FPGA.
- Removes runtime overhead
- Achieves deterministic, microsecond-level latency through parallel execution
- Runs seamlessly alongside existing FPGA control logic, with no additional hardware required
HDL generation is not the core innovation — but it acts as a multiplier, ensuring the optimized model can fully exploit the hardware.
Benchmark: MAX® 10 FPGA vs. Jetson Orin Nano
The whitepaper presents a direct comparison:
| Metric | Altera MAX® 10 + ONE AI | Nvidia Jetson Orin Nano (VGG19) | Improvement |
|---|---|---|---|
| Test Accuracy | 99.5% (INT8) | 88% (FP32) | 24× higher accuracy |
| Power | 0.5 W | 10 W | 20× lower power |
| Latency | 0.086 ms | 42 ms | 488× lower latency |
| Cost | €454 | €2505 | 6× lower cost |
| Throughput | 1736 FPS | 24 FPS | 72× higher FPS |
| Size | 11×11 mm | 70×45 mm | 26× smaller footprint |
Even with decade-old FPGA technology, the optimized ONE AI model outperforms Nvidia’s Jetson Orin Nano across every dimension.
Implications for Edge AI
- Hardware becomes secondary: Performance depends less on raw compute and more on how well the AI model is optimized.
- Scalable deployment: Lower power and cost make it viable to scale across thousands of devices.
- Industrial-grade resilience: MAX® 10 devices offer unique features — on-chip ADCs, jitter tolerance, long lifecycle support — ideal for harsh environments.
- Future-proof AI: Instead of chasing ever-larger GPUs, companies can rely on leaner, domain-specific architectures that deliver more with less.
Conclusion
The potato chip inspection demo is just one example. The broader lesson is clear:
- ONE AI optimizes the architecture itself, achieving higher accuracy with far fewer resources.
- With HDL deployment, even a low-cost FPGA like MAX® 10 can surpass a modern GPU.
- The result is real-time, energy-efficient, and cost-effective AI — perfectly suited for industrial applications.