HGQ-LUT delivers a practical LUT-aware training framework with new tensor-based layers, heterogeneous quantization, and a resource surrogate that automates accuracy-efficiency trade-offs for FPGA DNN inference.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
StableHLO serves as a viable unified representation for cross-architecture performance modeling of distributed ML workloads, preserving relative trends while exposing fidelity trade-offs.
citing papers explorer
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HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference
HGQ-LUT delivers a practical LUT-aware training framework with new tensor-based layers, heterogeneous quantization, and a resource surrogate that automates accuracy-efficiency trade-offs for FPGA DNN inference.
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Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO
StableHLO serves as a viable unified representation for cross-architecture performance modeling of distributed ML workloads, preserving relative trends while exposing fidelity trade-offs.