#Experimental setup:
I have tested out a Neural Network for the task of object detection. The minimalist (python+opencv) code reads 1 single image (that is read only once initially) and loops over the same image to perform inference / detection 5k times using KSNN.
There are no other substantial processes running during the experiment.
#The problems: 1) Every 1000 iterations of object detection (on the same image), there are random sudden drops in NPU performance (peaks in latency).
2) The RAM usage grows substantially over time around 50-70% (looks like memory bloating or leakage going on?) and only partially drops every 1000 iterations of processing, (on the same test image, no additional imreads nor writes are involved).
Both of (1) NPU latency peaks and (2) partial clearance of the RAM also seem to be in sync.
As KSNN is somewhat of a black box, any help regarding this will be much appreciated: What is going on? How can I fix it?
In particular, apart from the cyclical memory clearance, there seems to be occasional memory leaks in the inference calls (cf. below for the memory profiling).
@justbob There is no development plan for the time being, we will open it at the right time. We do Python API mainly to provide an interface for ordinary users to experience NPU. If you have high requirements for efficiency, it is recommended that you use the C interface
