Abstract: Systems and methods for training a visual question answer model include training a teacher model by performing image conditional visual question generation on a visual language model (VLM) and a targeted visual question answer dataset using images to generate question and answer pairs. Unlabeled images are pseudolabeled using the teacher model to decode synthetic question and answer pairs for the unlabeled images. The synthetic question and answer pairs for the unlabeled images are merged with real data from the targeted visual question answer dataset to generate a self-augmented training set. A student model is trained using the VLM and the self-augmented training set to return visual answers to text queries.

Abstract: Methods and systems herein can efficiently interconnect processors through a custom grid (a data mesh) utilizing upper metal layer routing in a semiconductor die design to minimize latency. A computer-implemented method of routing interconnects on a semiconductor die includes receiving a set of non-default routes and associated routing rules; identifying a set of critical signals for feedthrough on the set of non-default routes; generating a connectivity matrix including a set of resulting routes, the resulting routes routing the set of critical signals through the set of non-default routes; generating a timing analysis of the connectivity matrix based on a set of latency requirements; responsive to determining that the timing analysis is not compliant with the latency requirements, generating a set of routing constraints; and updating the associated routing rules to include the set of routing constraints.