Etash Guha

Learned Cost Model for Placement on Reconfigurable Dataflow Hardware

Etash Guha
Tianxiao Jiang
Andrew Deng
Muthu Annamalai
Jian Zhang
Design Automation Conference (poster) 2022, 2022


Mapping a dataflow-graph of an ML model onto a reconfigurable system is difficult, as different mappings have different throughputs and consume resource constraints differently. To solve this, a model to evaluate the throughput of mappings is necessary as measuring throughput completely is expensive. Many use a hand-designed analytical model, relying on proxy features or intuition, introducing error. We provide a Learned Approach that predicts throughput 31\%-52\% more accurately over a variety of graphs. In addition, our approach shows no accuracy degradation after removing performance annotations. We show that using this approach results in 5.6\% faster compiled graphs.