LACO: A Latency-Constraint Offline Neural Network Scheduler towards Reliable Self-Driving Perception

Perception is a crucial and compute-intensive self-driving stage where multiple DNN models periodically process image and point-cloud inputs with constant intervals as latency constraints. This scenario is similar to the multistream scenario in the MLPerf benchmark but with more than one query sequence of diverse intervals from different sensors, and each query also evokes more than one model. In this paper, we call this scenario multi-multistream (M2-stream). Because the periodic inputs arrive continuously, the neural network processor should process them in time to avoid information dropping and maintain self-driving safety.