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Visual sensors combined with video analysis algorithms can enhance applications in surveillance, healthcare, intelligent vehicle control, human-machine interfaces, etc. Hardware solutions exist for video analysis. Analog on-sensor processing solutions feature image sensor integration. However, the precision loss of analog signal processing prevents those solutions from realizing complex algorithms, and they lack flexibility. Vision processors realize high GOPS numbers by combining a processor array for parallel operations and a decision processor for other ones. Converting from parallel data in the processor array to scalar in the decision processor creates a throughput bottleneck. Parallel memory accesses also lead to high power consumption. Privacy is a critical issue in setting up visual sensors because of the danger of revealing video data from image sensors or processors. These issues exist with the above solutions because inputting or outputting video data is inevitable.