4 papers
On the "Induction Bias" in Sequence Models
M. Reza Ebrahimi, Michaël Defferrard, Sunny Panchal +1
Despite the remarkable practical success of transformer-based language models, recent work has raised concerns about their ability to perform state tracking. In particular, a growi…
NeuroSteiner: A Graph Transformer for Wirelength Estimation
Sahil Manchanda, Dana Kianfar, Markus Peschl +2
A core objective of physical design is to minimize wirelength (WL) when placing chip components on a canvas. Computing the minimal WL of a placement requires finding rectilinear St…
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Natasha Butt, Blazej Manczak, Auke Wiggers +4
Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchma…
DE-HNN: An effective neural model for Circuit Netlist representation
Zhishang Luo, Truong Son Hy, Puoya Tabaghi +7
The run-time for optimization tools used in chip design has grown with the complexity of designs to the point where it can take several days to go through one design cycle which ha…