Vulcan —
Temporal Convolutional Boiler Sequencer
Vulcan manages multi-boiler lead/lag sequencing and modulation in commercial heating plants. Named for the Roman god of fire and forge. It predicts optimal boiler firing order, modulation rates, and hot water supply temperature reset curves based on building load, outdoor temperature, and return water conditions.
Abstract
Background
Vulcan manages multi-boiler lead/lag sequencing and modulation in commercial heating plants. Named for the Roman god of fire and forge. It predicts optimal boiler firing order, modulation rates, and hot water supply temperature reset curves based on building load, outdoor temperature, and return water conditions.
Approach
The model was trained in AxonML (a pure-Rust deep learning framework) and compiled through the Hailo Dataflow Compiler (DFC 3.33.1) targeting Hailo-8 silicon. Post-training INT8 quantization was applied during the DFC compilation pass with production telemetry calibration data. The resulting Hailo Executable Format (HEF) binary executes on Hailo’s fixed-function dataflow architecture with deterministic latency and zero framework overhead at the edge.
Results
On production hardware (Hailo-8 M.2 (P/N: HM218B1C2FAE, S/N: HLDDM2A234600289)), Vulcan achieves 10,145 FPS (hw_only) with 0.121 ms hardware latency at 2.24 W average power draw.
Conclusion
Vulcan is production-ready as a single HEF binary deployed to edge devices with no external dependencies beyond the HailoRT vendor runtime. The model meets real-time latency requirements for its target hvac control application.
| Model | Vulcan |
| Domain | HVAC Control |
| Architecture | Temporal Convolutional Boiler Sequencer |
| Target silicon | Hailo-8 |
| Measured on | Hailo-8 M.2 (P/N: HM218B1C2FAE, S/N: HLDDM2A234600289) |
| DFC compiler | 3.33.1 |
| Framework | AxonML v0.6 (pure-Rust, CUDA + CPU backends) |
| Author | Andrew Jewell Sr. · ORCID 0009-0005-2158-7060 |
| Organization | AutomataNexus LLC · Fort Wayne, Indiana |
Executive overview
Vulcan manages multi-boiler lead/lag sequencing and modulation in commercial heating plants. Named for the Roman god of fire and forge. It predicts optimal boiler firing order, modulation rates, and hot water supply temperature reset curves based on building load, outdoor temperature, and return water conditions.
Network I/O
Input: 14-channel boiler plant telemetry. Output: per-boiler stage commands + modulation targets.
Architecture
Temporal Convolutional Boiler Sequencer
6-layer dilated TCN processing boiler plant telemetry: individual boiler firing status, modulation levels, supply/return HW temperatures, outdoor air temperature, building load proxy (from BAS), pump differential pressure, and stack temperatures. The model outputs next-period boiler stage commands (which boilers to fire) and target modulation percentages for each active boiler, implementing predictive lead/lag rotation to equalize run hours.
Compilation constraints
All AxonML models targeting Hailo silicon are compiled under the fixed-function dataflow constraints: no dynamic control flow, no variable-length dimensions, all activations representable in INT8 after calibration, and no operations requiring dedicated softmax hardware (replaced with ReLU gating or depthwise convolution equivalents where necessary).
Silicon performance
Measured on production hardware via hailortcli benchmark with 5-second sustained inference. Device: Hailo-8 M.2 (P/N: HM218B1C2FAE, S/N: HLDDM2A234600289).
| Metric | Measured Value |
|---|---|
| FPS (hw_only) | 10,145.20 |
| FPS (streaming) | 10,145.50 |
| HW Latency | 0.121000 ms |
| Power (streaming avg) | 2.23900 W |
| Power (streaming max) | 2.24600 W |
| Power (idle) | 0.74979 W |
| Quantization | INT8 (post-training, DFC calibration) |
| DFC Compiler | 3.33.1 |
| HailoRT | 4.20.0 |
| Measured On | Hailo-8 M.2 (P/N: HM218B1C2FAE, S/N: HLDDM2A234600289) |
Deployment
Deployed as a single HEF binary. No ONNX runtime, TensorFlow Lite, or Python inference stack required at the edge.
| Target silicon | Hailo-8 |
| Measured on | Hailo-8 M.2 (P/N: HM218B1C2FAE, S/N: HLDDM2A234600289) |
| DFC compiler | 3.33.1 |
| Quantization | INT8 (post-training, production telemetry calibration) |
| Runtime | HailoRT (vendor runtime) |
| Edge platform | Raspberry Pi 5 + Hailo AI HAT+ (M.2 Key M) |
Deployment procedure
Copy the .hef binary to the target device. hailortcli run loads the HEF directly into the Hailo-8 dataflow engine over PCIe. Inference begins immediately with deterministic per-frame latency. No model conversion, graph optimization, or warmup phase required.
References
- Jewell, A. (2026). AxonML: A Pure-Rust Deep Learning Framework for Edge Inference. AutomataNexus LLC. Technical whitepaper.
- Hailo Technologies Ltd. (2024). Hailo Dataflow Compiler User Guide. DFC v3.33.1.
- Hailo Technologies Ltd. (2024). Hailo-8 Product Datasheet.
AutomataNexus LLC · Fort Wayne, Indiana · andrew.jewellsr@automatanexus.com
Andrew Jewell Sr. · ORCID 0009-0005-2158-7060
May 2026 · All rights reserved.