Abstract: Aspects of the disclosure are directed to automatically determining floor planning in chips, which factors in memory macro alignment. A deep reinforcement learning (RL) agent can be trained to determine optimal placements for the memory macros, where memory macro alignment can be included as a regularization cost to be added to the placement objective as a RL reward. Tradeoffs between the placement objective and alignment of macros can be controlled by a tunable alignment parameter.

Abstract: Methods and apparatus are described herein for selecting, from one or more electronic data sources, a candidate textual statement associated with an entity, identifying one or more attributes of the candidate textual statement; and determining, based on the identified one or more attributes of the candidate textual statement, a measure of suitability of the candidate textual statement for presentation as a testimonial about the entity.