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FPGA-based GCN accelerator co-optimizes sparsification and approximation for energy efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel FPGA-based accelerator for Graph Convolutional Networks (GCNs) designed for energy-efficient inference on edge devices. This system integrates graph sparsification with approximate computing techniques, specifically 8-bit quantization and approximate multipliers, on an AMD Kria KV260 platform. Evaluations on datasets like Cora and Amazon Photo demonstrated significant speedups, up to 9.88x, while maintaining high classification accuracy and keeping power consumption below 1W. The study highlights that co-optimizing graph sparsification and approximate arithmetic is key to achieving efficient low-power GCN inference. AI

IMPACT Enables more efficient and lower-power deployment of graph neural networks on edge devices.

RANK_REASON This is a research paper detailing a novel hardware-software co-design for GCN inference on FPGAs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FPGA-based GCN accelerator co-optimizes sparsification and approximation for energy efficiency

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This is a research paper detailing a novel hardware-software co-design for GCN inference on FPGAs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference

    Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-constrained edge platforms remains challenging due to the computational and memory demands of sparse graph aggregation. This work pre…

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