Edge AI & Vision Pipeline

01The Problem

Cloud inference doesn't work when connectivity is intermittent or latency is unacceptable. Running ML models naively on edge hardware burns power, heats the enclosure, and still misses throughput targets. INT8 quantization done wrong degrades accuracy beyond tolerance.

02Our Approach

1. 01TensorRT FP16/INT8 optimization pipeline — calibration dataset driven quantization
2. 02GStreamer zero-copy DMA buffer pipeline — frame stays in GPU-accessible memory end-to-end
3. 03Multi-stream NvInfer: run 4 cameras at 30fps at same power as 1 camera naive
4. 04NPU offloading on Hailo-8: 26 TOPS dedicated inference, CPU completely free
5. 05Power profiling: thermal throttle prevention via DVFS tuning

03Verified Metrics

All measurements on production-grade hardware, oscilloscope verified

To evaluate your Edge AI & Vision Pipeline needs on your own platform, schedule an embedded architecture audit or scope your platform class with the system requirements calculator.