{"ID":2852487,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01872","arxiv_id":"2511.01872","title":"Learned Cost Model for Placement on Reconfigurable Dataflow Hardware","abstract":"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.","short_abstract":"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-de...","url_abs":"https://arxiv.org/abs/2511.01872","url_pdf":"https://arxiv.org/pdf/2511.01872v1","authors":"[\"Etash Guha\",\"Tianxiao Jiang\",\"Andrew Deng\",\"Jian Zhang\",\"Muthu Annamalai\"]","published":"2025-10-21T22:45:45Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.LG\",\"cs.PL\"]","methods":"[]","has_code":false}