Edge AI & Vision Pipeline

⚠The 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.

Our Approach

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

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