Ariadne (Aegis) —
Residual Conv2D Fingerprint Classifier (Production)
Production deployment of the Ariadne fingerprint classifier on Hailo-10H. Running at 3,882 FPS on the NexusWatch platform, enabling multi-reader fingerprint verification with sub-millisecond latency per scan.
Abstract
Background
Production deployment of the Ariadne fingerprint classifier on Hailo-10H. Running at 3,882 FPS on the NexusWatch platform, enabling multi-reader fingerprint verification with sub-millisecond latency per scan.
Approach
The model was trained in AxonML (a pure-Rust deep learning framework) and compiled through the Hailo Dataflow Compiler (DFC 5.3.0) targeting Hailo-10H 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-10H (NexusWatch, FW 5.3.0)), Ariadne (Aegis) achieves 3,882 FPS with 46.9 °C average die temperature (max 47.3 °C).
Conclusion
Ariadne (Aegis) 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 biometric security application.
| Model | Ariadne (Aegis) |
| Domain | Biometric Security |
| Architecture | Residual Conv2D Fingerprint Classifier (Production) |
| Target silicon | Hailo-10H |
| Measured on | Hailo-10H (NexusWatch, FW 5.3.0) |
| DFC compiler | 5.3.0 |
| 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
Production deployment of the Ariadne fingerprint classifier on Hailo-10H. Running at 3,882 FPS on the NexusWatch platform, enabling multi-reader fingerprint verification with sub-millisecond latency per scan.
Network I/O
Input: grayscale fingerprint [1, 1, 128, 128]. Output: 256-dim identity embedding.
Architecture
Residual Conv2D Fingerprint Classifier (Production)
9-block deep residual Conv2D with depthwise-separable convolutions. 128x128 input. On Hailo-10H the larger model (vs Argus) benefits more from the additional compute resources, achieving throughput suitable for deployment scenarios with multiple concurrent fingerprint readers.
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-10H (NexusWatch, FW 5.3.0).
| Metric | Measured Value |
|---|---|
| FPS (streaming) | 3,881.96 |
| Die Temperature (mean) | 46.93 °C |
| Die Temperature (min) | 45.51 °C |
| Die Temperature (max) | 47.33 °C |
| Quantization | INT8 (post-training, DFC calibration) |
| DFC Compiler | 5.3.0 |
| HailoRT | 5.3.0 |
| Measured On | Hailo-10H (NexusWatch, FW 5.3.0) |
Deployment
Deployed as a single HEF binary. No ONNX runtime, TensorFlow Lite, or Python inference stack required at the edge.
| Target silicon | Hailo-10H |
| Measured on | Hailo-10H (NexusWatch, FW 5.3.0) |
| DFC compiler | 5.3.0 |
| 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-10H 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 v5.3.0.
- Hailo Technologies Ltd. (2024). Hailo-10H Product Datasheet.
AutomataNexus LLC · Fort Wayne, Indiana · andrew.jewellsr@automatanexus.com
Andrew Jewell Sr. · ORCID 0009-0005-2158-7060
May 2026 · All rights reserved.