Abstract: An estimation model utilizes simulations of an autonomous vehicle and objects detected near the automated vehicle to develop estimates of tolerable frame processing latency to develop real world frame processing latency estimates for similar driving conditions. An estimation model can a minimum tolerable latency for processing the frames of image data of an object detection camera on an autonomous vehicle using the object state data of the objects detected near the autonomous vehicle. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, then a safety check is failed and an alert is sent. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, then the hardware resources are prioritized to the processing for that sensor.

Abstract: A method of training an N-bit neural network (N?2), is proposed to include: providing the N-bit neural network that includes a plurality of weights to be trained, each of the weights being composed of N bits that respectively correspond to N bit orders which are divided into multiple bit order groups, wherein the bits of the weights are divided, based on the bit orders to which the bits of the weights correspond, into multiple bit groups that respectively correspond to the bit order groups; and determining the weights for the N-bit neural network by training the bit groups one by one.