Late Breaking Results: Attention in Graph2Seq Neural Networks towards Push-Button Analog IC Placement
TimeWednesday, December 8th6:00pm - 7:00pm PST
LocationLevel 2 - Lobby
Event Type
Late Breaking Results Poster
Networking Reception
Virtual Programs
Presented In-Person
DescriptionIn this paper, disruptive research using modern embedding techniques and an attention-based encoder-decoder deep learning model is conducted to automate analog layout synthesis. The attention-based Graph2Seq is inherently independent of the number of devices within a circuit topology and their order, and, its training does not rely on expensive legacy layout data but only on sizing solutions. Results show that the proposed model generates placement solutions at push-button speed and can generalize to circuit topologies and technological nodes not used in training. Moreover, the model produces solutions that compete with highly optimized analog placements and other, order-dependent and non-scalable, models.