CODO automates comprehensive dataflow optimization on FPGAs, achieving 1.45x-4.52x speedups on kernels and up to 33.8x on DNN models over state-of-the-art frameworks.
Comba: A comprehensive model-based analysis framework for high level synthesis of real applications
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DiffHLS predicts HLS QoR via differential learning: separate GNN+LLM models for kernel baseline and design delta are composed to yield the final estimate, showing lower MAPE than GNN baselines on PolyBench.
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CODO: An Automated Compiler for Comprehensive Dataflow Optimization
CODO automates comprehensive dataflow optimization on FPGAs, achieving 1.45x-4.52x speedups on kernels and up to 33.8x on DNN models over state-of-the-art frameworks.
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DiffHLS: Differential Learning for High-Level Synthesis QoR Prediction with GNNs and LLM Code Embeddings
DiffHLS predicts HLS QoR via differential learning: separate GNN+LLM models for kernel baseline and design delta are composed to yield the final estimate, showing lower MAPE than GNN baselines on PolyBench.