Smart Glasses Distributed AI Inference

In modern artificial intelligence systems, distributed pipelines are used to achieve predictable latency, controlled memory use, reasonable power consumption, and reliable behavior. The architecture typically includes sensors, smart glasses, a Kotlin/Flutter interface, network/gateway, NVIDIA Jetson, ROS 2/Isaac ROS, and an NVIDIA AI model.

To optimize performance, a baseline must be established by recording key metrics such as end-to-end latency, processing FPS, CPU/GPU utilization, RAM/GPU memory, network RTT, dropped frames, temperature, and power/battery impact. Identifying bottlenecks involves measuring each stage separately: capture, transfer, decode, preprocess, inference, postprocess, and UI.

To control processing rate, only process the newest frame instead of accumulating stale frames. Separating workloads into high, medium, and low priority ensures that safety commands are not blocked by non-critical tasks. Reducing unnecessary data copies, such as unnecessary conversions between YUV, RGB, Bitmap, and JPEG, can help optimize performance.

Bounded queues prevent temporary overloads from causing stale latency. Profiling the target hardware, such as Android/Flutter or Jetson, with performance tools can help optimize the system. Validating sustained performance through longer tests and recording performance curves helps identify potential issues like thermal throttling or memory pressure.

Testing failure conditions, such as weak network, disconnected devices, high CPU load, GPU memory pressure, low battery, and thermal throttling, ensures the system degrades gracefully. Creating a benchmark table with actual measurements of FPS, latency, RAM, GPU utilization, and temperature allows for a clear comparison before and after optimization.

Applying domain-specific optimizations, such as choosing the right glasses, phone, Jetson or cloud, optimizing sampling, compression, caching, and battery measurement, is crucial for improving performance. The recommended optimization sequence involves measuring the current pipeline, removing unnecessary work, reducing data movement, bounding queues, moving expensive work away from UI/control threads, using hardware acceleration where supported, re-measuring accuracy and latency, running a sustained test, testing failure behavior, and documenting the improvement.

Protecting the physical control loop is essential, as AI models should produce validated perception, plans, or intents, with deterministic safety and control layers enforcing physical constraints.