One way to achieve higher video frame inference rates in situations where no state is maintained between frames is to split an incoming video stream across multiple inference pipelines. The new rt-ai Edge Scaler Stream Processing Element (SPE) does exactly that. The screen capture above shows the design and the real time performance information (in the windows on the right). The pipelines in this case are just single SPEs running single shot object detection on the Intel NCS 2. The CSSD SPE is able to process around 13 1280 x 720 frames per second by itself. Using the Scaler SPE to leverage two CSSD SPEs, each with one NCS 2 running on different nodes, the throughput has been doubled. In fact, performance should scale roughly linearly with the number of pipelines attached.
The Scaler SPE implements a health check function that determines the availability of pipelines at run time. Only pipelines that pass the health check are eligible to receive frames to be processed. In the example, Scaler can support eight pipelines but only two are connected (2 and 6) so only these pass the health check and receive frames. Frames from the In port are distributed across the active pipelines in a round robin fashion.
Pipelines are configured with a maximum to the number of in-flight frames in order to maximize pipeline throughput and minimize latency. Without multiple in-flight frames, CSSD performance would be roughly halved. In fact, pipelines can have different processing throughputs – the Scaler SPE automatically adjusts pipeline usage based on achieved throughput. Result messages may be received from pipelines out of sequence and the Scaler SPE ensures that the final output stream on the Out port has been reordered correctly.
The Scaler SPE can actually support any type of data (not just video) and any type of pipeline (not just inference) provided there is no retained state between messages. This makes it a very useful new building block.