Edge video surveillance is undergoing a fundamental shift. Sending high-resolution feeds to central clouds for inference is no longer viable due to bandwidth constraints and zero-trust security postures. Local processing is the only secure answer.
Local Inference Over Cloud Dependencies
Our latest Titanium series hardware embeds a dedicated Neural Processing Unit (NPU) capable of delivering up to 26 TOPS of localized AI computation. By executing object detection, license plate recognition (LPR), and classification models directly on the camera, we prevent the exposure of unencrypted video streams over the network.
"Under zero-trust policies, raw video never leaves the camera. Only lightweight metadata hashes are securely dispatched to the telemetry register, cutting bandwidth usage by 98%."
Optimized Model Quantization
To run deep learning models efficiently on low-power edge chips, our models are quantized from FP32 to INT8 precision. Our custom quantization calibration pipeline ensures less than 1.5% degradation in classification accuracy while tripling inference speed and reducing thermal signatures.
