DiagramNet supplies a new multimodal dataset and progressive training pipeline with decoupled multi-agent workflow, allowing a 3B model to outperform GPT-5, Claude-Sonnet-4, and Gemini-2.5-Pro by over 2x on system-level diagram tasks while generalizing to other benchmarks.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
LLM evaluation for RTL generation identifies three performance tiers with frontier models reaching high synthesis quality and reveals systematic failure differences between proprietary and open models.
HyperX is the first end-to-end FPGA accelerator for Nyström-based HDC graph classification, delivering 6.85× speedup and 169× energy efficiency over CPU baselines plus 3.4% average accuracy gain on TUDataset benchmarks.
DeepFedNAS delivers up to 1.21% higher accuracy and 61x faster architecture search for federated learning on heterogeneous IoT by replacing random supernet sampling with Pareto-optimal elite architectures and using a multi-objective fitness function as a zero-cost proxy.
FusionCell fuses DeiT-processed layout geometry with graph-transformer netlist topology via topology-guided cross-attention to predict six standard-cell metrics at 0.92% average MAPE on a 19.5k-cell 7nm dataset.
GNNs succeed in EDA when their propagation, aggregation, and supervision match the native algebra of each circuit task, such as max-plus recurrences for timing or hypergraph penalties for placement.
citing papers explorer
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DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams
DiagramNet supplies a new multimodal dataset and progressive training pipeline with decoupled multi-agent workflow, allowing a 3B model to outperform GPT-5, Claude-Sonnet-4, and Gemini-2.5-Pro by over 2x on system-level diagram tasks while generalizing to other benchmarks.
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Synthesis-in-the-Loop Evaluation of LLMs for RTL Generation: Quality, Reliability, and Failure Modes
LLM evaluation for RTL generation identifies three performance tiers with frontier models reaching high synthesis quality and reveals systematic failure differences between proprietary and open models.
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Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA
HyperX is the first end-to-end FPGA accelerator for Nyström-based HDC graph classification, delivering 6.85× speedup and 169× energy efficiency over CPU baselines plus 3.4% average accuracy gain on TUDataset benchmarks.
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DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Training
DeepFedNAS delivers up to 1.21% higher accuracy and 61x faster architecture search for federated learning on heterogeneous IoT by replacing random supernet sampling with Pareto-optimal elite architectures and using a multi-objective fitness function as a zero-cost proxy.
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FusionCell: Cross-Attentive Fusion of Layout Geometry and Netlist Topology for Standard-Cell Performance Prediction
FusionCell fuses DeiT-processed layout geometry with graph-transformer netlist topology via topology-guided cross-attention to predict six standard-cell metrics at 0.92% average MAPE on a 19.5k-cell 7nm dataset.
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Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
GNNs succeed in EDA when their propagation, aggregation, and supervision match the native algebra of each circuit task, such as max-plus recurrences for timing or hypergraph penalties for placement.