# First Production HVAC Deployment: 8-Model Neural Network Suite on Hailo-8 NPU
**Hello Hailo Community!**
I’m excited to share what I believe is the first production deployment of Hailo-8 NPUs in the HVAC industry, and possibly one of the first real-world production use cases showcased in this community.
## **Production Deployment Overview**
- **Industry**: Building Management Systems (HVAC)
- **Scale**: 15+ active facilities, 106+ equipment units
- **Daily Processing**: 500,000+ predictions at 11Hz
- **Hardware**: Custom Raspberry Pi 5 + Hailo-8 Build (details below)
- **Platform**: Integrated into AutomataNexus BMS Platform
- **Cost Impact**: 95% reduction in hardware costs
## **Hardware Configuration**
- **Raspberry Pi 5** (16GB RAM variant)
- **52Pi Dual M.2 Slot HAT**
- **Sabrent Rocket Nano 1TB 2242 M.2 SSD** (NVMe)
- **Hailo-8 AI Chip** (standalone module, not the RPi AI HAT Kit)
*Note: Using the standalone Hailo-8 chip with custom integration rather than the RPi AI HAT Kit for maximum flexibility and performance.*
## **The Nexus BMS Neural Network Suite**
I’ve deployed an 8-model hierarchical AI system that handles comprehensive building management:
### **Specialist Models (5 Domain Experts)**
1. **AQUILO** - Electrical Systems (608K params, 96.7% accuracy)
2. **BOREAS** - Refrigeration (1.2M params, 91.91% accuracy)
3. **NAIAD** - Water Systems (533K params, 99.99% accuracy)
4. **VULCAN** - Mechanical (476K params, 98.1% accuracy)
5. **ZEPHYRUS** - Airflow (845K params, 99.8% accuracy)
### **Integration Layer**
6. **COLOSSUS** - Multi-Model Fusion (17.3M params, 100% accuracy)
7. **GAIA** - Safety Validation (2.4M params, 100% accuracy)
### **Master Controller**
8. **APOLLO** - Cost Prediction & Coordination (20.8M params, 99.92% accuracy)
## **Hailo-8 Performance Results**
**Why Hailo-8 was perfect for this deployment:**
- **Total Pipeline Latency**: ~91ms (all 8 models)
- **Inference Rate**: 11Hz continuous operation
- **Model Size**: 250MB (all 8 models quantized to INT8)
- **Uptime**: 99.9% availability in production
The Hailo-8’s 26 TOPS of performance allows us to run all 8 models simultaneously in real-time while maintaining ultra-low latency for critical HVAC control systems. The 16GB RAM on the Pi 5 handles all preprocessing and buffering, while the NVMe SSD provides lightning-fast model loading and data logging.
## **What Makes This Special**
**Fault Detection Coverage**: 79 unique fault types across all HVAC systems
- Electrical faults (overcurrent, phase imbalance, harmonics)
- Refrigeration issues (liquid slugging, low refrigerant, oil logging)
- Mechanical problems (bearing failure, misalignment, vibration)
- Water system anomalies (leaks, flow issues, pump health)
- Airflow optimization (filter degradation, duct analysis, IAQ)
**Edge-First Architecture**: Everything runs locally on the Hailo-8 - no cloud dependency, complete data privacy, zero latency for safety-critical decisions.
**Safety-Critical Validation**: GAIA model validates every automated action before execution - 8,029 safety overrides during testing with zero false negatives.
## **Technical Architecture**
The hierarchical approach maximizes the Hailo-8’s parallel processing capabilities:
```
Sensors (400+) → Preprocessing (Pi 5 CPU) → 5 Specialist Models (parallel on Hailo-8) →
COLOSSUS (fusion) → GAIA (safety) → APOLLO (decisions) → Actions
```
Each specialist model focuses on its domain expertise, COLOSSUS correlates cross-system issues, GAIA ensures safety, and APOLLO makes final cost-optimized decisions.
## **Certification & Validation**
I’ve created comprehensive Certificates of Authenticity for each model and the complete suite, validating their production readiness and AI capabilities. These will be attached to demonstrate the professional deployment standards.
## **Why This Matters for Hailo Community**
This deployment proves that Hailo-8 NPUs can:
1. **Handle Complex Multi-Model Systems** in production environments
2. **Deliver Consistent Performance** in mission-critical applications
3. **Replace Expensive Industrial Hardware** with dramatic cost savings
4. **Enable True Edge AI** without cloud dependencies
5. **Scale to Enterprise Deployments** across multiple facilities
## **Implementation Notes**
The 52Pi Dual M.2 HAT allows us to use both NVMe storage and the Hailo-8 module simultaneously. The 16GB RAM variant of the Pi 5 is crucial for handling the sensor buffer (1GB circular) and runtime memory requirements (~2GB for all models).
## **What’s Next**
Currently expanding to more sites and exploring integration with smart grid technologies. The combination of Hailo-8 performance and HVAC domain expertise is opening new possibilities in building intelligence.
## **Community Value**
I hope this real-world case study helps other developers see the production potential of Hailo-8 NPUs. Happy to answer technical questions about deployment, model optimization, or performance tuning.
**Has anyone else deployed Hailo-8 in production environments? Would love to hear about other real-world use cases!**
## **Get In Touch**
If you’re interested in learning more about the AutomataNexus BMS Platform, any other Reports , or any of my other projects, feel free to reach out. I’m always happy to discuss technical implementations, share insights, or explore potential collaborations.
-–
*Andrew G. Jewell Sr. | Founder & AI Systems Engineer | AutomataNexus LLC*
*Email: agjewell@currenthvac.net | devops@automatacontrols.com | DevOps@AutomataNexus.com*
*“Where Intelligence Meets Infrastructure”*
Love seeing production deployments like this in the HVAC space.