EDGE AI
On-Device Neural Inference
Compressed neural models running on embedded compute hardware — detecting, classifying, and tracking at mission-relevant latency without any uplink requirement. Optimized for the SWaP constraints of tactical unmanned platforms.
< 50 ms
End-to-end inference latency
< 15 W
Total compute envelope
INT8
Quantization target
3
Supported hardware targets
INFERENCE PIPELINE
Model compression to hardware deployment.
The inference stack processes neural models through a deterministic optimization pipeline before deployment — ensuring each model's latency and power budget is characterized before it reaches a platform.
Base Model
FP32 PyTorch / ONNX model from training pipeline
Compression
Structured pruning at 40% sparsity, INT8 post-training quantization
Characterization
Latency, power draw, and accuracy measured on target hardware
Runtime Deployment
TensorRT / HailoRT / custom FPGA bitstream — hardware-specific runtime
HARDWARE TARGETS
Three compute substrates. Different SWaP points.
Jetson Orin NX
NVIDIA embedded GPU / NPU
Hailo-8
Purpose-built AI inference chip
Custom FPGA
AMD Zynq UltraScale+ / programmable logic
LATENCY COMPARISON
Object detection inference time by hardware target.
YOLOv8n INT8 — 640×640 input — single-frame inference
ENGAGE
Characterizing edge compute for your platform's SWaP budget?
Latency, power draw, and accuracy are interlinked — and they depend on your specific sensor resolution, object classes, and frame rate requirements. Hardware target selection is a program-specific decision. We walk through the tradeoff space in a structured technical briefing. Kestrelsense does not build weapons systems; we build the inference substrate that informs them.
Request Technical Briefing